Pillow | Python Imaging Library | Computer Vision library

by python-pillow Python Version: 9.4.0 License: Non-SPDX

X-Ray Key Features Code Snippets(10)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Pillow Summary

Pillow is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning applications. Pillow has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However Pillow has a Non-SPDX License. You can install using 'pip install Pillow' or download it from GitHub, PyPI.

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

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Pillow has a medium active ecosystem.

It has 10665 star(s) with 2010 fork(s). There are 219 watchers for this library.

There were 2 major release(s) in the last 6 months.

There are 88 open issues and 2662 have been closed. On average issues are closed in 135 days. There are 26 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Pillow is 9.4.0

Pillow Support

Best in #Computer Vision

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Pillow Support

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Quality

Pillow has 0 bugs and 0 code smells.

Pillow Quality

Best in #Computer Vision

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Pillow Quality

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Pillow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Pillow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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Pillow Security

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Pillow has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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Pillow License

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Pillow releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Pillow saves you 19159 person hours of effort in developing the same functionality from scratch.

It has 40016 lines of code, 3149 functions and 279 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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Top functions reviewed by kandi - BETA

kandi has reviewed Pillow and discovered the below as its top functions. This is intended to give you an instant insight into Pillow implemented functionality, and help decide if they suit your requirements.

Builds the list of supported extensions .

Sets up attributes .

Convert the image .

Read the bitmap .

Load the default font data .

Save an image .

Write multiple frames to fp .

Extract a value from the data .

Add application marker .

Compute the regular polygon vertices .

Get all kandi verified functions for this library.

Pillow Key Features

Python Imaging Library (Fork)

Pillow Examples and Code Snippets

:class:.PngImagePlugin.iTXt Class

Python

Lines of Code : 0

License : Non-SPDX (NOASSERTION)

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                                    :param value: value for this key :param lang: language code :param tkey: UTF-8 version of the key name

What about PIL?

Python

Lines of Code : 0

License : Non-SPDX (NOASSERTION)

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                                    Prior to Pillow 2.0.0, very few image code changes were made. Pillow 2.0.0 added Python 3 support and includes many bug fixes from many contributors.

Font size and offset methods

Python

Lines of Code : 0

License : Non-SPDX (NOASSERTION)

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                                    from PIL import Image, ImageDraw, ImageFont
font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") width, height = font.getsize("Hello world") left, top = font.getoffset("Hello world")
im = Image.new("RGB", (100, 100)) draw = ImageDraw.Draw(im) width, height = draw.textsize("Hello world")
width, height = font.getsize_multiline("Hello\nworld") width, height = draw.multiline_textsize("Hello\nworld")
font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") left, top, right, bottom = font.getbbox("Hello world") width, height = right - left, bottom - top
im = Image.new("RGB", (100, 100)) draw = ImageDraw.Draw(im) width = draw.textlength("Hello world")
left, top, right, bottom = draw.multiline_textbbox((0, 0), "Hello\nworld") width, height = right - left, bottom - top

Pillow - Dds Image Plugin

Python

Lines of Code : 189

License : Non-SPDX

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                                    """ A Pillow loader for .dds files (S3TC-compressed aka DXTC) Jerome Leclanche  Documentation: https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt The contents of this file are hereby released in the public domain (CC0) Full text of the CC0 license: https://creativecommons.org/publicdomain/zero/1.0/ """ import struct from io import BytesIO from PIL import Image, ImageFile # Magic ("DDS ") DDS_MAGIC = 0x20534444 # DDS flags DDSD_CAPS = 0x1 DDSD_HEIGHT = 0x2 DDSD_WIDTH = 0x4 DDSD_PITCH = 0x8 DDSD_PIXELFORMAT = 0x1000 DDSD_MIPMAPCOUNT = 0x20000 DDSD_LINEARSIZE = 0x80000 DDSD_DEPTH = 0x800000 # DDS caps DDSCAPS_COMPLEX = 0x8 DDSCAPS_TEXTURE = 0x1000 DDSCAPS_MIPMAP = 0x400000 DDSCAPS2_CUBEMAP = 0x200 DDSCAPS2_CUBEMAP_POSITIVEX = 0x400 DDSCAPS2_CUBEMAP_NEGATIVEX = 0x800 DDSCAPS2_CUBEMAP_POSITIVEY = 0x1000 DDSCAPS2_CUBEMAP_NEGATIVEY = 0x2000 DDSCAPS2_CUBEMAP_POSITIVEZ = 0x4000 DDSCAPS2_CUBEMAP_NEGATIVEZ = 0x8000 DDSCAPS2_VOLUME = 0x200000 # Pixel Format DDPF_ALPHAPIXELS = 0x1 DDPF_ALPHA = 0x2 DDPF_FOURCC = 0x4 DDPF_PALETTEINDEXED8 = 0x20 DDPF_RGB = 0x40 DDPF_LUMINANCE = 0x20000 # dds.h DDS_FOURCC = DDPF_FOURCC DDS_RGB = DDPF_RGB DDS_RGBA = DDPF_RGB | DDPF_ALPHAPIXELS DDS_LUMINANCE = DDPF_LUMINANCE DDS_LUMINANCEA = DDPF_LUMINANCE | DDPF_ALPHAPIXELS DDS_ALPHA = DDPF_ALPHA DDS_PAL8 = DDPF_PALETTEINDEXED8 DDS_HEADER_FLAGS_TEXTURE = DDSD_CAPS | DDSD_HEIGHT | DDSD_WIDTH | DDSD_PIXELFORMAT DDS_HEADER_FLAGS_MIPMAP = DDSD_MIPMAPCOUNT DDS_HEADER_FLAGS_VOLUME = DDSD_DEPTH DDS_HEADER_FLAGS_PITCH = DDSD_PITCH DDS_HEADER_FLAGS_LINEARSIZE = DDSD_LINEARSIZE DDS_HEIGHT = DDSD_HEIGHT DDS_WIDTH = DDSD_WIDTH DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS_TEXTURE DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS_COMPLEX | DDSCAPS_MIPMAP DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS_COMPLEX DDS_CUBEMAP_POSITIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEX DDS_CUBEMAP_NEGATIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEX DDS_CUBEMAP_POSITIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEY DDS_CUBEMAP_NEGATIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEY DDS_CUBEMAP_POSITIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEZ DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEZ # DXT1 DXT1_FOURCC = 0x31545844 # DXT3 DXT3_FOURCC = 0x33545844 # DXT5 DXT5_FOURCC = 0x35545844 def _decode565(bits): a = ((bits >> 11) & 0x1F) << 3 b = ((bits >> 5) & 0x3F) << 2 c = (bits & 0x1F) << 3 return a, b, c def _c2a(a, b): return (2 * a + b) // 3 def _c2b(a, b): return (a + b) // 2 def _c3(a, b): return (2 * b + a) // 3 def _dxt1(data, width, height): # TODO implement this function as pixel format in decode.c ret = bytearray(4 * width * height) for y in range(0, height, 4): for x in range(0, width, 4): color0, color1, bits = struct.unpack("> 2 if control == 0: r, g, b = r0, g0, b0 elif control == 1: r, g, b = r1, g1, b1 elif control == 2: if color0 > color1: r, g, b = _c2a(r0, r1), _c2a(g0, g1), _c2a(b0, b1) else: r, g, b = _c2b(r0, r1), _c2b(g0, g1), _c2b(b0, b1) elif control == 3: if color0 > color1: r, g, b = _c3(r0, r1), _c3(g0, g1), _c3(b0, b1) else: r, g, b = 0, 0, 0 idx = 4 * ((y + j) * width + x + i) ret[idx : idx + 4] = struct.pack("4B", r, g, b, 255) return bytes(ret) def _dxtc_alpha(a0, a1, ac0, ac1, ai): if ai <= 12: ac = (ac0 >> ai) & 7 elif ai == 15: ac = (ac0 >> 15) | ((ac1 << 1) & 6) else: ac = (ac1 >> (ai - 16)) & 7 if ac == 0: alpha = a0 elif ac == 1: alpha = a1 elif a0 > a1: alpha = ((8 - ac) * a0 + (ac - 1) * a1) // 7 elif ac == 6: alpha = 0 elif ac == 7: alpha = 0xFF else: alpha = ((6 - ac) * a0 + (ac - 1) * a1) // 5 return alpha def _dxt5(data, width, height): # TODO implement this function as pixel format in decode.c ret = bytearray(4 * width * height) for y in range(0, height, 4): for x in range(0, width, 4): a0, a1, ac0, ac1, c0, c1, code = struct.unpack("<2BHI2HI", data.read(16)) r0, g0, b0 = _decode565(c0) r1, g1, b1 = _decode565(c1) for j in range(4): for i in range(4): ai = 3 * (4 * j + i) alpha = _dxtc_alpha(a0, a1, ac0, ac1, ai) cc = (code >> 2 * (4 * j + i)) & 3 if cc == 0: r, g, b = r0, g0, b0 elif cc == 1: r, g, b = r1, g1, b1 elif cc == 2: r, g, b = _c2a(r0, r1), _c2a(g0, g1), _c2a(b0, b1) elif cc == 3: r, g, b = _c3(r0, r1), _c3(g0, g1), _c3(b0, b1) idx = 4 * ((y + j) * width + x + i) ret[idx : idx + 4] = struct.pack("4B", r, g, b, alpha) return bytes(ret) class DdsImageFile(ImageFile.ImageFile): format = "DDS" format_description = "DirectDraw Surface" def _open(self): if not _accept(self.fp.read(4)): raise SyntaxError("not a DDS file") (header_size,) = struct.unpack("

Pillow - anchors

Python

Lines of Code : 23

License : Non-SPDX

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                                    from PIL import Image, ImageDraw, ImageFont font = ImageFont.truetype("Tests/fonts/NotoSans-Regular.ttf", 16) def test(anchor): im = Image.new("RGBA", (200, 100), "white") d = ImageDraw.Draw(im) d.line(((100, 0), (100, 100)), "gray") d.line(((0, 50), (200, 50)), "gray") d.text((100, 50), f"{anchor} example", "black", font, anchor) return im if __name__ == "__main__": im = Image.new("RGBA", (600, 300), "white") d = ImageDraw.Draw(im) for y, row in enumerate( (("ma", "mt", "mm"), ("ms", "mb", "md"), ("ls", "ms", "rs")) ): for x, anchor in enumerate(row): im.paste(test(anchor), (x * 200, y * 100)) if x != 0: d.line(((x * 200, y * 100), (x * 200, (y + 1) * 100)), "black", 3) if y != 0: d.line(((x * 200, y * 100), ((x + 1) * 200, y * 100)), "black", 3) im.save("docs/example/anchors.png") im.show()

Improve speed of getpixel and putpixel

Python

Lines of Code : 102

License : Strong Copyleft (CC BY-SA 4.0)

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#!/usr/bin/env python3

from PIL import Image
import numba as nb
import numpy as np

def Rainbow(i):
    x = 1 - abs(((i / 60) % 2) - 1)
    i %= 360
    if (i >= 0   and i < 60 ): r,g,b = 1, x, 0
    if (i >= 60  and i < 120): r,g,b = x, 1, 0
    if (i >= 120 and i < 180): r,g,b = 0, 1, x
    if (i >= 180 and i < 240): r,g,b = 0, x, 1
    if (i >= 240 and i < 300): r,g,b = x, 0, 1
    if (i >= 300 and i < 360): r,g,b = 1, 0, x
    res = (int(r * 255), int(g * 255), int(b * 255))
    return res

def RainbowFilter(img):
    for x in range(img.size[0]):
        for y in range(img.size[1]):
            intensity = sum(img.getpixel((x, y)))
            img.putpixel((x, y), Rainbow(intensity + x + y))
    return img

@nb.njit(parallel=True)
def imahdi(img):    
    intensity = img.sum(axis=-1)
    row , col = img.shape[:2]
    for r in nb.prange(row):
        for c in nb.prange(col):
            i = (intensity[r,c] + r + c)
            x = 1 - abs(((i / 60) % 2) - 1)
            i %= 360
            res = np.zeros(3)
            if   (i >= 0   and i < 60 ): res = np.array([1, x, 0])
            elif (i >= 60  and i < 120): res = np.array([x, 1, 0])
            elif (i >= 120 and i < 180): res = np.array([0, 1, x])
            elif (i >= 180 and i < 240): res = np.array([0, x, 1])
            elif (i >= 240 and i < 300): res = np.array([x, 0, 1])
            elif (i >= 300 and i < 360): res = np.array([1, 0, x])
            img[r,c] = res * 255
    return img

@nb.njit(parallel=True)
def mark(img):    
    intensity = img.sum(axis=-1)
    row , col = img.shape[:2]
    # Create zeroed result image
    res = np.zeros_like(img)
    for r in nb.prange(row):
        # Only make outer loop parallel else inner one will make more threads than cores
        for c in range(col):
            i = (intensity[r,c] + r + c)
            x = 1 - abs(((i / 60) % 2) - 1)
            x = int(x * 255)
            i %= 360
            # Split the problem space in half in one test - like binary search
            if i < 180:
               if i < 60:
                  # Don't create whole new array here 
                  # Don't assign zeroes, array is empty already
                  res[r,c,0] = 255
                  res[r,c,1] = x
               elif i < 120:
                  res[r,c,0] = x
                  res[r,c,1] = 255
               else:
                  res[r,c,1] = 255
                  res[r,c,2] = x
            else:
               if i < 240:
                  res[r,c,1] = x
                  res[r,c,2] = 255
               elif i < 300:
                  res[r,c,0] = x
                  res[r,c,2] = 255
               else:
                  res[r,c,0] = 255
                  res[r,c,2] = x
    return res

orig = Image.open('cat.jpg')
res  = RainbowFilter(orig)
res.save('result.png')

im  = np.asarray(orig)
res = imahdi(im)
Image.fromarray(res).save('imahdi.ppm')

res = mark(im)
Image.fromarray(res).save('mark.ppm')

In [17]: %timeit res = RainbowFilter(orig)                                      
11.7 s ± 80 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [18]: %timeit res = imahdi(im)                                               
1.52 s ± 4.81 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [13]: %timeit res=mark(im)                                                   
35.6 ms ± 928 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

PIL Image not cropping the way I want

Python

Lines of Code : 26

License : Strong Copyleft (CC BY-SA 4.0)

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import PIL
from PIL import ImageEnhance
from PIL import Image

image =  Image.open("img.jpg").convert("RGB")

contact_sheet = PIL.Image.new(image.mode,(1920,1080))

enhancer = ImageEnhance.Color(image)

images = []
current_location = 0   
for i in range(10):
    images.append(enhancer.enhance(i/10))

for img in images:
    # Changed here slightly, current_slice is what we want to paste into the new image
    current_slice = img.crop((current_location,0,current_location+192,0+1080))
    contact_sheet.paste(current_slice,(current_location,0))
    current_location+=192

#display(contact_sheet)
imagesave = contact_sheet.save("CroppedContactSheet.jpg")

AttributeError when displaying image on streamlit web app

Python

Lines of Code : 43

License : Strong Copyleft (CC BY-SA 4.0)

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import pm4py
import streamlit as st
# from PIL import Image
import pandas as pd
import io


d = {
    'case_id': [1, 1, 1, 2],
    'activity_id': ['Accepted', 'Awaiting Documentation',
    'Complete Activated', 'Approved'],
    'timestamp': ['2021-12-20T15:52:47', '2021-12-20T14:57:58',
    '2021-12-20T14:59:14', '2021-12-20T14:57:59']
}

@st.cache
def prepare_df(df):
    df1 = df[["case_id", "activity_id", "timestamp"]]
    df1["case_id"] = df1["case_id"].astype(str)
    df1["activity_id"] = df1["activity_id"].astype(str)
    df_log = pm4py.format_dataframe(df1, case_id='case_id', activity_key='activity_id', timestamp_key='timestamp')
    return df_log

st.title('pm4py on streamlit')

df = pd.DataFrame(d)

df_log = prepare_df(df)

bpm_discovery = st.container()

map2 = pm4py.discover_heuristics_net(df_log)

# 1. Save to file and show it by streamlit.
fn = 'a.png'
pm4py.save_vis_heuristics_net(map2, fn)

# 2. Save to memory.
# image2 = io.BytesIO(pm4py.view_heuristics_net(map2))

with bpm_discovery:
     st.image(fn, caption='Heuristic Minners algorithm')

How can I fix my python code to save and resize images using glob on linux

Python

Lines of Code : 10

License : Strong Copyleft (CC BY-SA 4.0)

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from PIL import Image
import glob

images=glob.glob("*.jpg")

for im in images:
    # print(im)
    img = Image.open(im)
    img = img.resize((100, 100), Image.ANTIALIAS)
    img.save(im+'_resized.jpg')

PIL doesn't detect identical images

Python

Lines of Code : 62

License : Strong Copyleft (CC BY-SA 4.0)

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#!/usr/bin/env python3

import numpy as np
from PIL import Image

# Load base image, extract alpha channel and bounding box and crop to it
base = Image.open('base.png').getchannel('A')
bbox = base.getbbox()
print(f'Base image bbox: {bbox}')
base = base.crop(bbox)
base.save('DEBUG-base.png')

# Load overlying layers, extracting alpha channel, cropping and making into Numpy arrays
layers = []
for layer in [0,1,2]:
   filename = f'layer{layer}.png'
   print(f'Loading {filename}')
   im = Image.open(filename).getchannel('A').crop(bbox)
   im.save(f'DEBUG-{filename}')
   layers.append(np.array(im))

rm DEBUG*.png

# Make PIL Image of base layer into Numpy array
base = np.array(base)

# So we now have base and all layers, just alpha channels, cropped to bounding box as Numpy arrays
# We want to find areas where:
# a) base is non-transparent - we call this "maskA" and store as Numpy array
# b) any layer fully opaque - we call this "maskB" and store as Numpy array
maskA = base > 0
Image.fromarray((maskA*255).astype(np.uint8)).save('DEBUG-maskA.png')
maskB = (layers[0] == 255) | (layers[1] == 255) | (layers[2] == 255)
Image.fromarray((maskB*255).astype(np.uint8)).save('DEBUG-maskB.png')

# result is where there IS something in base layer and there is NO overlay that is fully opaque
res = maskA & ~maskB
Image.fromarray((res*255).astype(np.uint8)).save('result.png')

# Reduce result to simple Yes/No by seeing if any pixel is True
print(f'Result: {np.any(res)}')

Base image bbox: (80, 800, 301, 951)
Loading layer0.png
Loading layer1.png
Loading layer2.png
Result: True

#!/bin/bash
magick -size 2000x2000 xc:none -fill red  -draw "rectangle 80,800 300,950"    PNG32:base.png
magick -size 2000x2000 xc:none -fill blue -draw "rectangle 280,700 480,850"   PNG32:layer0.png  
magick -size 2000x2000 xc:none -fill lime -draw "rectangle 320,1000 460,1200" PNG32:layer1.png
magick -size 2000x2000 xc:none -fill orange -draw "rectangle 500,800 800,900" PNG32:layer2.png
magick base.png layer0.png -composite layer1.png -composite layer2.png -composite PNG32:case1.png

#!/bin/bash
magick -size 2000x2000 xc:none -fill red  -draw "rectangle 80,800 300,950"    PNG32:base.png
magick -size 2000x2000 xc:none -fill blue -draw "rectangle 70,700 350,900"    PNG32:layer0.png  
magick -size 2000x2000 xc:none -fill lime -draw "rectangle 50,900 250,1200" PNG32:layer1.png
magick -size 2000x2000 xc:none -fill orange -draw "rectangle 250,900 800,1200" PNG32:layer2.png
magick base.png layer0.png -composite layer1.png -composite layer2.png -composite PNG32:case2.png

Community Discussions

Trending Discussions on Pillow

Improve speed of getpixel and putpixel

Find the minimum plane of the maximum number of black pixels in the bottom image. Find the peak of the black pixels for the top image

unable to install pyodbc using python 3.10 in windows 10

Colab: (0) UNIMPLEMENTED: DNN library is not found

How to install local package with conda

ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

reticulate segfaults with call to plt.plot()

django rest Error - AttributeError: module 'collections' has no attribute 'MutableMapping'

Can't deploy streamlit app on share.streamlit.io

Could not find a version that satisfies the requirement psycopg2

Trending Discussions on Pillow

Improve speed of getpixel and putpixel

Find the minimum plane of the maximum number of black pixels in the bottom image. Find the peak of the black pixels for the top image

unable to install pyodbc using python 3.10 in windows 10

Colab: (0) UNIMPLEMENTED: DNN library is not found

How to install local package with conda

ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

reticulate segfaults with call to plt.plot()

django rest Error - AttributeError: module 'collections' has no attribute 'MutableMapping'

Can't deploy streamlit app on share.streamlit.io

Could not find a version that satisfies the requirement psycopg2

QUESTION

Improve speed of getpixel and putpixel

Asked 2022-Apr-04 at 10:13

Using PIL, I'm applying a rainbow filter to the given image using getpixel and setpixel. One issue, this method is very slow. It takes around 10 seconds to finish one image.

def Rainbow(i):
    x = 1 - abs(((i / 60) % 2) - 1)
    i %= 360
    if (i >= 0   and i < 60 ): r,g,b = 1, x, 0
    if (i >= 60  and i < 120): r,g,b = x, 1, 0
    if (i >= 120 and i < 180): r,g,b = 0, 1, x
    if (i >= 180 and i < 240): r,g,b = 0, x, 1
    if (i >= 240 and i < 300): r,g,b = x, 0, 1
    if (i >= 300 and i < 360): r,g,b = 1, 0, x
    res = (int(r * 255), int(g * 255), int(b * 255))
    return res
def RainbowFilter(img):
    for x in range(img.size[0]):
        for y in range(img.size[1]):
            intensity = sum(img.getpixel((x, y)))
            img.putpixel((x, y), Rainbow(intensity + x + y))
    return img

im = Image.open('cat.jpg')
rainbow_im = RainbowFilter(im)
rainbow_im.save('rainbow_im.png')

Can you help me improve my specific algorithm, using exclusively Numpy or Pillow features, to resolve the issue mentioned?

ANSWER

Answered 2022-Apr-03 at 15:21

You can convert image to NumPy.array then use numba for improving speed like below:

from PIL import Image
import numba as nb
import numpy as np

@nb.njit(parallel=True)
def new_RainbowFilter(img):    
    intensity = img.sum(axis=-1)
    row , col = img.shape[:2]
    for r in nb.prange(row):
        for c in nb.prange(col):
            i = (intensity[r,c] + r + c)
            x = 1 - abs(((i / 60) % 2) - 1)
            i %= 360
            res = np.zeros(3)
            if   (i >= 0   and i < 60 ): res = np.array([1, x, 0])
            elif (i >= 60  and i < 120): res = np.array([x, 1, 0])
            elif (i >= 120 and i < 180): res = np.array([0, 1, x])
            elif (i >= 180 and i < 240): res = np.array([0, x, 1])
            elif (i >= 240 and i < 300): res = np.array([x, 0, 1])
            elif (i >= 300 and i < 360): res = np.array([1, 0, x])
            img[r,c] = res * 255
    return img


im = Image.open('cat.jpg')
img = np.asarray(im)
img = new_RainbowFilter(img)
im = Image.fromarray(img)
im.save('rainbow_im.png')

Source https://stackoverflow.com/questions/71724930

QUESTION

Find the minimum plane of the maximum number of black pixels in the bottom image. Find the peak of the black pixels for the top image

Asked 2022-Mar-22 at 18:00

This is the actual Image

I want to find the distance between 2 irregular edges in a binary image. That is I have marked with red on the below image. My idea was to draw a colored line (say red) on both the edges and then calculate the distance between them at 10 equal intervals (yellow-colored marking)

I cropped the image into two. Say the top half is

Bottom half is

I want to draw the two red lines or just find the distance between those two somehow. I have been using OpenCV and PILLOW for a lot of steps.

there are also image instances where at few columns there are no black pixels in the top image. How do i calculate the distance of the black pixels only from the top? Just the top image.

ANSWER

Answered 2022-Mar-22 at 18:00

Here is one possible approach. I will leave the details to you. But the idea is to use Numpy argmax (or argmin as appropriate) to get the index of the first white after all black in each column. First I flip the image vertically so that the black is at the top. Numpy argmax, seems to find the first white value along the column.

Input (bottom image):

import numpy as np
import cv2

# read image
img = cv2.imread("img.png")

# Note that there is a 2 pixel tall white line at the bottom
# Note that there is columns of black at the left and right sides

# convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold
img = cv2.threshold(img,0,255,cv2.THRESH_BINARY)[1]

# flip vertically
img = cv2.flip(img, 0)

# remove 2 pixels at the top
img = img[2:, :]

# add one pixel row of white at bottom
img = cv2.copyMakeBorder(img, 0, 2, 0, 0, cv2.BORDER_CONSTANT, value=255)

# find max along each column
max = np.argmax(img, axis=0)
print(max.shape)

print(max)

cv2.imshow('flipped',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Here is the output:

(709,)
[112 112 112 112  32  32  32  32  32  32  32  32  32  32  32  32  32  32
  31  31  31  31  31  31  31  31  31  31  30  30  30  30  30  30  30  30
  30  30  30  30  30  30  30  30  30  30  30  31  31  31  31  31  31  31
  31  31  31  31  31  31  31  31  31  30  30  30  30  30  30  30  30  30
  30  30  30  31  31  32  32  33  33  34  35  35  36  36  36  36  36  36
  36  36  36  36  36  36  36  36  37  37  38  38  39  39  39  40  40  40
  41  41  43  43  44  45  45  46  46  46  46  46  46  46  46  46  46  46
  46  46  46  46  46  46  46  46  46  46  46  46  46  45  45  45  45  44
  44  44  44  44  44  44  44  44  44  44  44  44  43  43  43  43  43  43
  43  43  43  43  43  43  43  44  44  44  44  44  44  44  44  43  43  43
  42  42  42  42  42  42  42  42  42  42  41  41  40  40  40  40  40  40
  40  40  40  40  40  40  40  40  40  40  40  40  39  39  39  39  39  39
  39  39  39  39  39  39  39  39  39  39  39  39  38  38  37  37  37  37
  37  37  37  36  36  36  36  36  35  35  35  34  34  34  34  34  34  34
  34  34  33  33  32  32  32  32  32  32  32  32  32  32  32  32  32  32
  33  33  34  34  35  35  36  37  37  37  37  38  38  38  38  38  38  38
  38  38  38  38  38  38  38  39  39  40  40  40  40  40  39  39  39  39
  39  39  38  38  38  37  37  37  37  37  37  37  37  37  37  37  37  37
  37  38  38  38  39  40  40  41  41  41  39  39  38  37  37  37  37  37
  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  38  38
  38  39  39  39  39  39  39  39  39  41  41  41  41  41  41  41  41  41
  41  41  41  41  41  41  41  41  41  41  41  41  41  41  41  41  41  41
  40  40  40  40  40  40  40  40  40  40  39  39  38  38  37  37  37  37
  36  36  36  36  36  36  35  35  34  34  34  33  33  33  32  31  31  31
  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31  31
  31  31  31  31  31  31  31  31  30  30  29  29  29  28  28  28  28  27
  27  26  26  26  26  26  26  26  26  26  26  26  26  26  26  27  27  27
  27  27  27  27  27  27  28  28  28  28  28  28  28  28  28  28  28  28
  28  28  28  28  28  28  28  28  28  28  28  28  28  28  29  29  29  29
  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29  29
  29  28  28  27  27  27  27  27  27  27  27  27  27  27  27  27  28  28
  28  28  29  29  30  30  30  31  31  32  32  33  33  34  34  35  35  36
  36  36  37  37  37  38  38  39  39  39  39  39  39  39  39  39  39  39
  39  38  38  37  37  37  37  37  37  36  36  35  35  35  35  35  35  35
  35  35  35  35  35  35  35  35  35  35  35  36  36  36  36  36  36  36
  36  36  36  36  36  36  37  37  37  37  37  37  37  37  37  37  37  37
  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37
  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37  37
  37  37  37  37  37  37  37  37  37  37  37  37  37  38  38  38  39  39
  39  39  39  40 112 112 112]

For the top use argmin or invert the polarity using img=255-img and then use argmax.

Source https://stackoverflow.com/questions/71574600

QUESTION

unable to install pyodbc using python 3.10 in windows 10

Asked 2022-Feb-24 at 20:07

I get this Error when I try to install Pyodbc , I have already install visual studio and I have Microsoft Visual C++ 12 , 15-19 in my machine but still its giving this error.

Running setup.py clean for pyodbc
Failed to build pyodbc
Installing collected packages: sqlparse, pytz, asgiref, pyodbc, Django, Pillow, mssql-django, django-crispy-forms
    Running setup.py install for pyodbc ... error
    ERROR: Command errored out with exit status 1:
     command: 'C:\Users\Athar\Desktop\New folder\Project\HeatlhCare\venv\Scripts\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\Athar\\AppData\\Local\\Temp\\pip-install-w0wwm18g\\pyodbc_61963e883a8543fea24a63b1c522bbea\\setup.py'"'"'; __file__='"'"'C:\\Users\\Athar\\AppData\\Local\\Temp\\pip-install-w0wwm18g\\pyodbc_61963e883a8543fea24a63b1c522bbea\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\Athar\AppData\Local\Temp\pip-record-t1td50y6\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\Athar\Desktop\New folder\Project\HeatlhCare\venv\include\site\python3.10\pyodbc'
         cwd: C:\Users\Athar\AppData\Local\Temp\pip-install-w0wwm18g\pyodbc_61963e883a8543fea24a63b1c522bbea\
    Complete output (7 lines):
    running install
    C:\Users\Athar\Desktop\New folder\Project\HeatlhCare\venv\lib\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
      warnings.warn(
    running build
    running build_ext
    building 'pyodbc' extension
    error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
    ----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\Users\Athar\Desktop\New folder\Project\HeatlhCare\venv\Scripts\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\Athar\\AppData\\Local\\Temp\\pip-install-w0wwm18g\\pyodbc_61963e883a8543fea24a63b1c522bbea\\setup.py'"'"'; __file__='"'"'C:\\Users\\Athar\\AppData\\Local\\Temp\\pip-install-w0wwm18g\\pyodbc_61963e883a8543fea24a63b1c522bbea\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\Athar\AppData\Local\Temp\pip-record-t1td50y6\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\Athar\Desktop\New folder\Project\HeatlhCare\venv\include\site\python3.10\pyodbc' Check the logs for full command output.

ANSWER

Answered 2021-Nov-12 at 13:38

The current release of pyodbc (4.0.32) does not have pre-built wheel files for Python 3.10. The easiest way to get it installed at the moment is to download the appropriate wheel from

https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyodbc

and then install it. For example, if you are running 64-bit Python then you would download the 64-bit wheel and use

pip install pyodbc‑4.0.32‑cp310‑cp310‑win_amd64.whl

Source https://stackoverflow.com/questions/69943254

QUESTION

Colab: (0) UNIMPLEMENTED: DNN library is not found

Asked 2022-Feb-08 at 19:27

I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:

Graph execution error:

2 root error(s) found.
  (0) UNIMPLEMENTED:  DNN library is not found.
     [[{{node functional_1/conv1_conv/Conv2D}}]]
     [[StatefulPartitionedCall/SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/Reshape_5/_126]]
  (1) UNIMPLEMENTED:  DNN library is not found.
     [[{{node functional_1/conv1_conv/Conv2D}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_restored_function_body_27380] ***

Never happended before.

Before I can run my model I have to install Tensor Flow object detection API with this command:

import os

os.chdir('/project/models/research')

!protoc object_detection/protos/*.proto --python_out=.
!cp object_detection/packages/tf2/setup.py .
!python -m pip install .

This is the output of command:

Processing /content/gdrive/MyDrive/models/research
  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
Collecting avro-python3
  Downloading avro-python3-1.10.2.tar.gz (38 kB)
Collecting apache-beam
  Downloading apache_beam-2.35.0-cp37-cp37m-manylinux2010_x86_64.whl (9.9 MB)
     |████████████████████████████████| 9.9 MB 1.6 MB/s
Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2)
Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (4.2.6)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (3.2.2)
Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.29.27)
Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.5)
Collecting tf-slim
  Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)
     |████████████████████████████████| 352 kB 50.5 MB/s
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.15.0)
Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.0.4)
Collecting lvis
  Downloading lvis-0.5.3-py3-none-any.whl (14 kB)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.4.1)
Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.3.5)
Collecting tf-models-official>=2.5.1
  Downloading tf_models_official-2.8.0-py2.py3-none-any.whl (2.2 MB)
     |████████████████████████████████| 2.2 MB 38.3 MB/s
Collecting tensorflow_io
  Downloading tensorflow_io-0.24.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.4 MB)
     |████████████████████████████████| 23.4 MB 1.7 MB/s
Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.7.0)
Collecting opencv-python-headless
  Downloading opencv_python_headless-4.5.5.62-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.7 MB)
     |████████████████████████████████| 47.7 MB 74 kB/s
Collecting sacrebleu
  Downloading sacrebleu-2.0.0-py3-none-any.whl (90 kB)
     |████████████████████████████████| 90 kB 10.4 MB/s
Requirement already satisfied: kaggle>=1.3.9 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.5.12)
Requirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (5.4.8)
Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.1.3)
Collecting tensorflow-addons
  Downloading tensorflow_addons-0.15.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
     |████████████████████████████████| 1.1 MB 37.8 MB/s
Requirement already satisfied: gin-config in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.5.0)
Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.0.1)
Collecting sentencepiece
  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
     |████████████████████████████████| 1.2 MB 37.5 MB/s
Collecting tensorflow-model-optimization>=0.4.1
  Downloading tensorflow_model_optimization-0.7.0-py2.py3-none-any.whl (213 kB)
     |████████████████████████████████| 213 kB 42.7 MB/s
Collecting pyyaml<6.0,>=5.1
  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)
     |████████████████████████████████| 636 kB 53.3 MB/s
Collecting tensorflow-text~=2.8.0
  Downloading tensorflow_text-2.8.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.9 MB)
     |████████████████████████████████| 4.9 MB 46.1 MB/s
Requirement already satisfied: google-api-python-client>=1.6.7 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.12.10)
Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.19.5)
Requirement already satisfied: tensorflow-hub>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.12.0)
Collecting seqeval
  Downloading seqeval-1.2.2.tar.gz (43 kB)
     |████████████████████████████████| 43 kB 2.1 MB/s
Collecting tensorflow~=2.8.0
  Downloading tensorflow-2.8.0-cp37-cp37m-manylinux2010_x86_64.whl (497.5 MB)
     |████████████████████████████████| 497.5 MB 28 kB/s
Collecting py-cpuinfo>=3.3.0
  Downloading py-cpuinfo-8.0.0.tar.gz (99 kB)
     |████████████████████████████████| 99 kB 10.1 MB/s
Requirement already satisfied: google-auth<3dev,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.35.0)
Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.0.1)
Requirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.17.4)
Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.0.4)
Requirement already satisfied: google-api-core<3dev,>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.26.3)
Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (57.4.0)
Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2018.9)
Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.54.0)
Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.23.0)
Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (21.3)
Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.17.3)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3dev,>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.2.8)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3dev,>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (4.8)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3dev,>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (4.2.4)
Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (2021.10.8)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (1.24.3)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (2.8.2)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (4.62.3)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (5.0.2)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.0.7)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3dev,>=1.16.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.4.8)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (2.10)
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Collecting tf-estimator-nightly==2.8.0.dev2021122109
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Collecting tensorboard<2.9,>=2.8
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Collecting fastavro<2,>=0.21.4
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Collecting requests<3.0.0dev,>=2.18.0
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Collecting dill<0.3.2,>=0.3.1.1
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Collecting numpy>=1.15.4
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Collecting orjson<4.0
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Collecting hdfs<3.0.0,>=2.1.0
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Collecting tensorflow-io-gcs-filesystem>=0.23.1
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     |████████████████████████████████| 2.1 MB 40.9 MB/s
Building wheels for collected packages: object-detection, py-cpuinfo, dill, avro-python3, seqeval
  Building wheel for object-detection (setup.py) ... done
  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1686316 sha256=775b8c34c800b3b3139d1067abd686af9ce9158011fccfb5450ccfd9bf424a5a
  Stored in directory: /tmp/pip-ephem-wheel-cache-rmw0fvil/wheels/d0/e3/e9/b9ffe85019ec441e90d8ff9eddee9950c4c23b7598204390b9
  Building wheel for py-cpuinfo (setup.py) ... done
  Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22257 sha256=ac956c4c039868fdba78645bea056754e667e8840bea783ad2ca75e4d3e682c6
  Stored in directory: /root/.cache/pip/wheels/d2/f1/1f/041add21dc9c4220157f1bd2bd6afe1f1a49524c3396b94401
  Building wheel for dill (setup.py) ... done
  Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78544 sha256=d9c6cdfd69aea2b4d78e6afbbe2bc530394e4081eb186eb4f4cd02373ca739fd
  Stored in directory: /root/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f
  Building wheel for avro-python3 (setup.py) ... done
  Created wheel for avro-python3: filename=avro_python3-1.10.2-py3-none-any.whl size=44010 sha256=4eca8b4f30e4850d5dabccee36c40c8dda8a6c7e7058cfb7f0258eea5ce7b2b3
  Stored in directory: /root/.cache/pip/wheels/d6/e5/b1/6b151d9b535ee50aaa6ab27d145a0104b6df02e5636f0376da
  Building wheel for seqeval (setup.py) ... done
  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=0ddfa46d0e36e9be346a90833ef11cc0d38cc7e744be34c5a0d321f997a30cba
  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
Successfully built object-detection py-cpuinfo dill avro-python3 seqeval
Installing collected packages: requests, protobuf, numpy, tf-estimator-nightly, tensorflow-io-gcs-filesystem, tensorboard, keras, tensorflow, portalocker, dill, colorama, tf-slim, tensorflow-text, tensorflow-model-optimization, tensorflow-addons, seqeval, sentencepiece, sacrebleu, pyyaml, pymongo, py-cpuinfo, proto-plus, orjson, opencv-python-headless, hdfs, fastavro, tf-models-official, tensorflow-io, lvis, avro-python3, apache-beam, object-detection
  Attempting uninstall: requests
    Found existing installation: requests 2.23.0
    Uninstalling requests-2.23.0:
      Successfully uninstalled requests-2.23.0
  Attempting uninstall: protobuf
    Found existing installation: protobuf 3.17.3
    Uninstalling protobuf-3.17.3:
      Successfully uninstalled protobuf-3.17.3
  Attempting uninstall: numpy
    Found existing installation: numpy 1.19.5
    Uninstalling numpy-1.19.5:
      Successfully uninstalled numpy-1.19.5
  Attempting uninstall: tensorflow-io-gcs-filesystem
    Found existing installation: tensorflow-io-gcs-filesystem 0.23.1
    Uninstalling tensorflow-io-gcs-filesystem-0.23.1:
      Successfully uninstalled tensorflow-io-gcs-filesystem-0.23.1
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.7.0
    Uninstalling tensorboard-2.7.0:
      Successfully uninstalled tensorboard-2.7.0
  Attempting uninstall: keras
    Found existing installation: keras 2.7.0
    Uninstalling keras-2.7.0:
      Successfully uninstalled keras-2.7.0
  Attempting uninstall: tensorflow
    Found existing installation: tensorflow 2.7.0
    Uninstalling tensorflow-2.7.0:
      Successfully uninstalled tensorflow-2.7.0
  Attempting uninstall: dill
    Found existing installation: dill 0.3.4
    Uninstalling dill-0.3.4:
      Successfully uninstalled dill-0.3.4
  Attempting uninstall: pyyaml
    Found existing installation: PyYAML 3.13
    Uninstalling PyYAML-3.13:
      Successfully uninstalled PyYAML-3.13
  Attempting uninstall: pymongo
    Found existing installation: pymongo 4.0.1
    Uninstalling pymongo-4.0.1:
      Successfully uninstalled pymongo-4.0.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.20.3 which is incompatible.
multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed apache-beam-2.35.0 avro-python3-1.10.2 colorama-0.4.4 dill-0.3.1.1 fastavro-1.4.9 hdfs-2.6.0 keras-2.8.0 lvis-0.5.3 numpy-1.20.3 object-detection-0.1 opencv-python-headless-4.5.5.62 orjson-3.6.6 portalocker-2.3.2 proto-plus-1.19.9 protobuf-3.19.4 py-cpuinfo-8.0.0 pymongo-3.12.3 pyyaml-5.4.1 requests-2.27.1 sacrebleu-2.0.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorboard-2.8.0 tensorflow-2.8.0 tensorflow-addons-0.15.0 tensorflow-io-0.24.0 tensorflow-io-gcs-filesystem-0.24.0 tensorflow-model-optimization-0.7.0 tensorflow-text-2.8.1 tf-estimator-nightly-2.8.0.dev2021122109 tf-models-official-2.8.0 tf-slim-1.1.0

I am noticing that this command uninstalling tensorflow 2.7 and installing tensorflow 2.8. I am not sure it was happening before. Maybe it's the reason DNN library link is missing o something?

I can confirm these:

Nothing was changed inside pretrained model or already installed model or object_detection source files I downloaded a year ago.
I tried to run command !pip install dnn - not working
I tried to restart runtime (without disconnecting) - not working

Somebody can help? Thanks.

ANSWER

Answered 2022-Feb-07 at 09:19

It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason

Source https://stackoverflow.com/questions/71000120

QUESTION

How to install local package with conda

Asked 2022-Feb-05 at 04:16

I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.

But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.

So I did the following:

write a simple meta.yaml so I could use conda-build to build jive locally
build jive with conda build .
I looked at the tarball that was produced and it does indeed contain the jive source as expected
In my other project, add jive to the dependencies in environment.yml, and add 'local' to the list of channels.
create a conda env using that environment.yml.

When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.) How can I fix the build/install so that this works?

Here is the meta.yaml, which lives in the jive project top level directory:

package:
  name: jive
  version: "0.2.1"

source:
  path: .

build:
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  host:
     - python>=3.5
     - pip
     - setuptools
  run:
     - python>=3.5
     - numpy
     - pandas
     - scipy
     - seaborn
     - matplotlib
     - scikit-learn
     - statsmodels
     - joblib
     - bokeh

test:
  imports: jive

And here is the output of conda build .

No numpy version specified in conda_build_config.yaml.  Falling back to default numpy value of 1.16
WARNING:conda_build.metadata:No numpy version specified in conda_build_config.yaml.  Falling back to default numpy value of 1.16
Adding in variants from internal_defaults
INFO:conda_build.variants:Adding in variants from internal_defaults
Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
Attempting to finalize metadata for jive
INFO:conda_build.metadata:Attempting to finalize metadata for jive
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
BUILD START: ['jive-0.2.1-py310_0.tar.bz2']
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla


The following NEW packages will be INSTALLED:

    bzip2:           1.0.8-h1de35cc_0
    ca-certificates: 2021.10.26-hecd8cb5_2
    certifi:         2021.5.30-py310hecd8cb5_0
    libcxx:          12.0.0-h2f01273_0
    libffi:          3.3-hb1e8313_2
    ncurses:         6.3-hca72f7f_2
    openssl:         1.1.1m-hca72f7f_0
    pip:             21.2.4-py310hecd8cb5_0
    python:          3.10.0-hdfd78df_3
    readline:        8.1.2-hca72f7f_1
    setuptools:      58.0.4-py310hecd8cb5_0
    sqlite:          3.37.0-h707629a_0
    tk:              8.6.11-h7bc2e8c_0
    tzdata:          2021e-hda174b7_0
    wheel:           0.37.1-pyhd3eb1b0_0
    xz:              5.2.5-h1de35cc_0
    zlib:            1.2.11-h4dc903c_4

Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Copying /Users/thomaskeefe/Documents/py_jive to /opt/miniconda3/conda-bld/jive_1642185595622/work/
source tree in: /opt/miniconda3/conda-bld/jive_1642185595622/work
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla
export BUILD_PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_build_env
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/work
Processing $SRC_DIR
  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
Building wheels for collected packages: jive
  Building wheel for jive (setup.py): started
  Building wheel for jive (setup.py): finished with status 'done'
  Created wheel for jive: filename=jive-0.2.1-py3-none-any.whl size=46071 sha256=b312955cb2fd917bc4e684a575407b884190680f2dddad7fcb9ac25e5b290fc9
  Stored in directory: /private/tmp/pip-ephem-wheel-cache-rbpkt2an/wheels/15/68/82/4ed7cd246fbc4c72cf764b425a03230247589bd2394a7e457b
Successfully built jive
Installing collected packages: jive
Successfully installed jive-0.2.1

Resource usage statistics from building jive:
   Process count: 3
   CPU time: Sys=0:00:00.3, User=0:00:00.5
   Memory: 53.7M
   Disk usage: 50.4K
   Time elapsed: 0:00:06.1


Packaging jive
INFO:conda_build.build:Packaging jive
INFO conda_build.build:build(2289): Packaging jive
Packaging jive-0.2.1-py310_0
INFO:conda_build.build:Packaging jive-0.2.1-py310_0
INFO conda_build.build:bundle_conda(1529): Packaging jive-0.2.1-py310_0
compiling .pyc files...
number of files: 70
Fixing permissions
INFO :: Time taken to mark (prefix)
        0 replacements in 0 files was 0.06 seconds
TEST START: /opt/miniconda3/conda-bld/osx-64/jive-0.2.1-py310_0.tar.bz2
Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO conda_build.variants:_combine_spec_dictionaries(234): Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO:conda_build.utils:Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO conda_build.utils:shutil_move_more_retrying(2091): Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO:conda_build.utils:shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO conda_build.utils:shutil_move_more_retrying(2098): shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol


The following NEW packages will be INSTALLED:

    blas:              1.0-mkl
    bokeh:             2.4.2-py39hecd8cb5_0
    bottleneck:        1.3.2-py39he3068b8_1
    brotli:            1.0.9-hb1e8313_2
    ca-certificates:   2021.10.26-hecd8cb5_2
    certifi:           2021.10.8-py39hecd8cb5_2
    cycler:            0.11.0-pyhd3eb1b0_0
    fonttools:         4.25.0-pyhd3eb1b0_0
    freetype:          2.11.0-hd8bbffd_0
    giflib:            5.2.1-haf1e3a3_0
    intel-openmp:      2021.4.0-hecd8cb5_3538
    jinja2:            3.0.2-pyhd3eb1b0_0
    jive:              0.2.1-py310_0            local
    joblib:            1.1.0-pyhd3eb1b0_0
    jpeg:              9d-h9ed2024_0
    kiwisolver:        1.3.1-py39h23ab428_0
    lcms2:             2.12-hf1fd2bf_0
    libcxx:            12.0.0-h2f01273_0
    libffi:            3.3-hb1e8313_2
    libgfortran:       3.0.1-h93005f0_2
    libpng:            1.6.37-ha441bb4_0
    libtiff:           4.2.0-h87d7836_0
    libwebp:           1.2.0-hacca55c_0
    libwebp-base:      1.2.0-h9ed2024_0
    llvm-openmp:       12.0.0-h0dcd299_1
    lz4-c:             1.9.3-h23ab428_1
    markupsafe:        2.0.1-py39h9ed2024_0
    matplotlib:        3.5.0-py39hecd8cb5_0
    matplotlib-base:   3.5.0-py39h4f681db_0
    mkl:               2021.4.0-hecd8cb5_637
    mkl-service:       2.4.0-py39h9ed2024_0
    mkl_fft:           1.3.1-py39h4ab4a9b_0
    mkl_random:        1.2.2-py39hb2f4e1b_0
    munkres:           1.1.4-py_0
    ncurses:           6.3-hca72f7f_2
    numexpr:           2.8.1-py39h2e5f0a9_0
    numpy:             1.21.2-py39h4b4dc7a_0
    numpy-base:        1.21.2-py39he0bd621_0
    olefile:           0.46-pyhd3eb1b0_0
    openssl:           1.1.1m-hca72f7f_0
    packaging:         21.3-pyhd3eb1b0_0
    pandas:            1.3.5-py39h743cdd8_0
    patsy:             0.5.2-py39hecd8cb5_0
    pillow:            8.4.0-py39h98e4679_0
    pip:               21.2.4-py39hecd8cb5_0
    pyparsing:         3.0.4-pyhd3eb1b0_0
    python:            3.9.7-h88f2d9e_1
    python-dateutil:   2.8.2-pyhd3eb1b0_0
    pytz:              2021.3-pyhd3eb1b0_0
    pyyaml:            6.0-py39hca72f7f_1
    readline:          8.1.2-hca72f7f_1
    scikit-learn:      1.0.2-py39hae1ba45_0
    scipy:             1.7.3-py39h8c7af03_0
    seaborn:           0.11.2-pyhd3eb1b0_0
    setuptools:        58.0.4-py39hecd8cb5_0
    six:               1.16.0-pyhd3eb1b0_0
    sqlite:            3.37.0-h707629a_0
    statsmodels:       0.13.0-py39hca72f7f_0
    threadpoolctl:     2.2.0-pyh0d69192_0
    tk:                8.6.11-h7bc2e8c_0
    tornado:           6.1-py39h9ed2024_0
    typing_extensions: 3.10.0.2-pyh06a4308_0
    tzdata:            2021e-hda174b7_0
    wheel:             0.37.1-pyhd3eb1b0_0
    xz:                5.2.5-h1de35cc_0
    yaml:              0.2.5-haf1e3a3_0
    zlib:              1.2.11-h4dc903c_4
    zstd:              1.4.9-h322a384_0

Preparing transaction: ...working... done
Verifying transaction: ...working...
ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::intel-openmp-2021.4.0-hecd8cb5_3538, defaults/osx-64::llvm-openmp-12.0.0-h0dcd299_1
  path: 'lib/libiomp5.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'bin/webpinfo'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'bin/webpmux'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/decode.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/encode.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/mux.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/mux_types.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/types.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.7.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.3.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.3.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebp.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebpdecoder.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebpmux.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/cwebp.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/dwebp.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/webpinfo.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/webpmux.1'



done
Executing transaction: ...working...
ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/llvm-openmp-12.0.0-h0dcd299_1/lib/libiomp5.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libiomp5.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpinfo
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpinfo




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpmux
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpmux




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/decode.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/decode.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/encode.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/encode.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux_types.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux_types.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/types.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/types.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.7.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.7.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.3.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.3.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.3.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.3.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/1018f8ab-87a7-4fa8-a41c-4c14cc77cfff
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebp.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/e3701fae-f2cd-44e9-9dc6-c71f499cd2c2
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpdecoder.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/0f4bcf50-01e5-404d-b1a4-8a87d45c22c5
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpmux.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/cwebp.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/cwebp.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/dwebp.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/dwebp.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpinfo.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpinfo.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpmux.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpmux.1





    Installed package of scikit-learn can be accelerated using scikit-learn-intelex.
    More details are available here: https://intel.github.io/scikit-learn-intelex

    For example:

        $ conda install scikit-learn-intelex
        $ python -m sklearnex my_application.py



done
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp
Traceback (most recent call last):
  File "/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp/run_test.py", line 2, in 
    import jive
ModuleNotFoundError: No module named 'jive'
import: 'jive'
Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING:conda_build.build:Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING conda_build.build:tests_failed(2970): Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
TESTS FAILED: jive-0.2.1-py310_0.tar.bz2

EDIT: I added a test: section to the meta.yaml as merv suggested.

ANSWER

Answered 2022-Feb-05 at 04:16

The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.

I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).

Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").

Try adjusting the meta.yaml to something like

package:
  name: jive
  version: "0.2.1"

source:
  path: .

build:
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  host:
     - python
     - pip
     - setuptools
  run:
     - python
     - numpy
     - pandas
     - scipy
     - seaborn
     - matplotlib
     - scikit-learn
     - statsmodels
     - joblib
     - bokeh

If you want to use it in a Python 3.9 environment, then use conda build --python 3.9 ..

Source https://stackoverflow.com/questions/70705250

QUESTION

ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

Asked 2022-Jan-28 at 03:50

Error while installing manimce, I have been trying to install manimce library on windows subsystem for linux and after running

pip install manimce
Collecting manimce
  Downloading manimce-0.1.1.post2-py3-none-any.whl (249 kB)
     |████████████████████████████████| 249 kB 257 kB/s
Collecting Pillow
  Using cached Pillow-8.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)
Collecting scipy
  Using cached scipy-1.7.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.3 MB)
Collecting colour
  Using cached colour-0.1.5-py2.py3-none-any.whl (23 kB)
Collecting pangocairocffi<0.5.0,>=0.4.0
  Downloading pangocairocffi-0.4.0.tar.gz (17 kB)
  Preparing metadata (setup.py) ... done
Collecting numpy
  Using cached numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)
Collecting pydub
  Using cached pydub-0.25.1-py2.py3-none-any.whl (32 kB)
Collecting pygments
  Using cached Pygments-2.10.0-py3-none-any.whl (1.0 MB)
Collecting cairocffi<2.0.0,>=1.1.0
  Downloading cairocffi-1.3.0.tar.gz (88 kB)
     |████████████████████████████████| 88 kB 160 kB/s
  Preparing metadata (setup.py) ... done
Collecting tqdm
  Using cached tqdm-4.62.3-py2.py3-none-any.whl (76 kB)
Collecting pangocffi<0.9.0,>=0.8.0
  Downloading pangocffi-0.8.0.tar.gz (33 kB)
  Preparing metadata (setup.py) ... done
Collecting pycairo<2.0,>=1.19
  Using cached pycairo-1.20.1.tar.gz (344 kB)

  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting progressbar
  Downloading progressbar-2.5.tar.gz (10 kB)
  Preparing metadata (setup.py) ... done
Collecting rich<7.0,>=6.0
  Using cached rich-6.2.0-py3-none-any.whl (150 kB)
Collecting cffi>=1.1.0
  Using cached cffi-1.15.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (446 kB)
Collecting commonmark<0.10.0,>=0.9.0
  Using cached commonmark-0.9.1-py2.py3-none-any.whl (51 kB)
Collecting typing-extensions<4.0.0,>=3.7.4
  Using cached typing_extensions-3.10.0.2-py3-none-any.whl (26 kB)
Collecting colorama<0.5.0,>=0.4.0
  Using cached colorama-0.4.4-py2.py3-none-any.whl (16 kB)
Collecting pycparser
  Using cached pycparser-2.21-py2.py3-none-any.whl (118 kB)
Building wheels for collected packages: cairocffi, pangocairocffi, pangocffi, pycairo, progressbar
  Building wheel for cairocffi (setup.py) ... done
  Created wheel for cairocffi: filename=cairocffi-1.3.0-py3-none-any.whl size=89650 sha256=afc73218cc9fa1d844d7165f598e2be0428598166b4c3ed9de5bbdc94a0a6977
  Stored in directory: /home/yusifer_zendric/.cache/pip/wheels/f3/97/83/8022b9237866102e18d1b7ac0a269769e6fccba0f63dceb9b7
  Building wheel for pangocairocffi (setup.py) ... done
  Created wheel for pangocairocffi: filename=pangocairocffi-0.4.0-py3-none-any.whl size=19283 sha256=54399796259c6e24f9ab56c5747ab273dcf97fb6fed3e7b54935f9ac49351d50
  Stored in directory: /home/yusifer_zendric/.cache/pip/wheels/60/58/92/507a12a5044f7fcda6f4dfd8e0a607cc1fe957bc0dea885906
  Building wheel for pangocffi (setup.py) ... done
  Created wheel for pangocffi: filename=pangocffi-0.8.0-py3-none-any.whl size=37899 sha256=bea348af93696816b046dd901aa60d29a464460c5faac67628eb7e1ea7d1807d
  Stored in directory: /home/yusifer_zendric/.cache/pip/wheels/c4/df/6d/e9d0f79b1545f6e902cc22773b1429de7a5efc240b891ee009
  Building wheel for pycairo (pyproject.toml) ... error
  ERROR: Command errored out with exit status 1:
   command: /home/yusifer_zendric/manim_ce/venv/bin/python /home/yusifer_zendric/manim_ce/venv/lib/python3.8/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /tmp/tmpuguwzu3u
       cwd: /tmp/pip-install-l4hqdegr/pycairo_f4d80b8f3e4840a3802342825adcdff5
  Complete output (12 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.8
  creating build/lib.linux-x86_64-3.8/cairo
  copying cairo/__init__.py -> build/lib.linux-x86_64-3.8/cairo
  copying cairo/__init__.pyi -> build/lib.linux-x86_64-3.8/cairo
  copying cairo/py.typed -> build/lib.linux-x86_64-3.8/cairo
  running build_ext
  'pkg-config' not found.
  Command ['pkg-config', '--print-errors', '--exists', 'cairo >= 1.15.10']
  ----------------------------------------
  ERROR: Failed building wheel for pycairo
  Building wheel for progressbar (setup.py) ... done
  Created wheel for progressbar: filename=progressbar-2.5-py3-none-any.whl size=12074 sha256=7290ef8de5dd955bf756b90130f400dd19c2cc9ea050a5a1dce2803440f581e2
  Stored in directory: /home/yusifer_zendric/.cache/pip/wheels/2c/67/ed/d84123843c937d7e7f5ba88a270d11036473144143355e2747
Successfully built cairocffi pangocairocffi pangocffi progressbar
Failed to build pycairo
ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects
(venv) yusifer_zendric@Laptop-Yusifer:~/manim_ce$
(venv) yusifer_zendric@Laptop-Yusifer:~/manim_ce$ pip install manim_ce
ERROR: Could not find a version that satisfies the requirement manim_ce (from versions: none)
ERROR: No matching distribution found for manim_ce
(venv) yusifer_zendric@Laptop-Yusifer:~/manim_ce$ manim example_scenes/basic.py -pql

Command 'manim' not found, did you mean:

  command 'maim' from deb maim (5.5.3-1build1)

Try: sudo apt install 

(venv) yusifer_zendric@Laptop-Yusifer:~/manim_ce$ sudo apt-get install manim
[sudo] password for yusifer_zendric:
Reading package lists... Done
Building dependency tree
Reading state information... Done
E: Unable to locate package manim
(venv) yusifer_zendric@Laptop-Yusifer:~/manim_ce$ pip3 install manimlib
Collecting manimlib
  Downloading manimlib-0.2.0.tar.gz (4.8 MB)
     |████████████████████████████████| 4.8 MB 498 kB/s
  Preparing metadata (setup.py) ... done
Collecting Pillow
  Using cached Pillow-8.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)
Collecting argparse
  Downloading argparse-1.4.0-py2.py3-none-any.whl (23 kB)
Collecting colour
  Using cached colour-0.1.5-py2.py3-none-any.whl (23 kB)
Collecting numpy
  Using cached numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)
Collecting opencv-python
  Downloading opencv_python-4.5.4.60-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.3 MB)
     |████████████████████████████████| 60.3 MB 520 kB/s
Collecting progressbar
  Using cached progressbar-2.5-py3-none-any.whl
Collecting pycairo
  Using cached pycairo-1.20.1.tar.gz (344 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting pydub
  Using cached pydub-0.25.1-py2.py3-none-any.whl (32 kB)
Collecting pygments
  Using cached Pygments-2.10.0-py3-none-any.whl (1.0 MB)
Collecting scipy
  Using cached scipy-1.7.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.3 MB)
Collecting tqdm
  Using cached tqdm-4.62.3-py2.py3-none-any.whl (76 kB)
Building wheels for collected packages: manimlib, pycairo
  Building wheel for manimlib (setup.py) ... done
  Created wheel for manimlib: filename=manimlib-0.2.0-py3-none-any.whl size=212737 sha256=27efe2c226d80cfe5663928e980d3e5f5a164d8e9d0aacea5014d37ffdedb76a
  Stored in directory: /home/yusifer_zendric/.cache/pip/wheels/87/36/c1/2db5ed5de9908034108f3c39538cd3367445d9cec01e7c8c23
  Building wheel for pycairo (pyproject.toml) ... error
  ERROR: Command errored out with exit status 1:
   command: /home/yusifer_zendric/manim_ce/venv/bin/python /home/yusifer_zendric/manim_ce/venv/lib/python3.8/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /tmp/tmp5o2970su
       cwd: /tmp/pip-install-sxxp3lw2/pycairo_d372a62d0c6b4c4484391402d21485e1
  Complete output (12 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.8
  creating build/lib.linux-x86_64-3.8/cairo
  copying cairo/__init__.py -> build/lib.linux-x86_64-3.8/cairo
  copying cairo/__init__.pyi -> build/lib.linux-x86_64-3.8/cairo
  copying cairo/py.typed -> build/lib.linux-x86_64-3.8/cairo
  running build_ext
  'pkg-config' not found.
  Command ['pkg-config', '--print-errors', '--exists', 'cairo >= 1.15.10']
  ----------------------------------------
  ERROR: Failed building wheel for pycairo
Successfully built manimlib
Failed to build pycairo
ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

all the libraries are installed accept the pycairo library. It's just showing this to install pyproject.toml error. Infact I have already done pip install pyproject.toml and it is installed then also it's showing the same error.

ANSWER

Answered 2022-Jan-28 at 02:24

apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib  # or pip install manimlib

Then:

pip3 install manimce  # or pip install manimce

And everything works.

Source https://stackoverflow.com/questions/70508775

QUESTION

reticulate segfaults with call to plt.plot()

Asked 2022-Jan-26 at 16:45

I am encountering a segfault when I make a reticulated call to matplotlib.pyplot.plot().

Steps to produce error:

Create a Dockerfile with the contents:

FROM rocker/r-ver:latest

RUN apt update && apt install -y python3.8-venv python3.8-dev

RUN install2.r --error reticulate

COPY test.R /root/

Create a file test.R (in the same location) with the contents:

reticulate::virtualenv_create(
  envname = "./venv",
  packages = c("matplotlib")
)

reticulate::use_virtualenv("./venv")

reticulate::py_run_string("import matplotlib.pyplot as plt; plt.plot([1, 2, 3], [1, 2, 3])")

Build an image from the Dockerfile: docker build . --tag="segfault-reprex"

Try to run test.R in the running container: docker run segfault-reprex Rscript /root/test.R. This gives the full traceback listed below.

Full traceback

Using Python: /usr/bin/python3.8
Creating virtual environment './venv' ... Done!
Installing packages: 'pip', 'wheel', 'setuptools', 'matplotlib'
Collecting pip
  Downloading pip-21.3.1-py3-none-any.whl (1.7 MB)
Collecting wheel
  Downloading wheel-0.37.1-py2.py3-none-any.whl (35 kB)
Collecting setuptools
  Downloading setuptools-60.5.0-py3-none-any.whl (958 kB)
Collecting matplotlib
  Downloading matplotlib-3.5.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.3 MB)
Collecting kiwisolver>=1.0.1
  Downloading kiwisolver-1.3.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.2 MB)
Collecting fonttools>=4.22.0
  Downloading fonttools-4.28.5-py3-none-any.whl (890 kB)
Collecting packaging>=20.0
  Downloading packaging-21.3-py3-none-any.whl (40 kB)
Collecting cycler>=0.10
  Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting numpy>=1.17
  Downloading numpy-1.22.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)
Collecting pillow>=6.2.0
  Downloading Pillow-9.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)
Collecting python-dateutil>=2.7
  Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Collecting pyparsing>=2.2.1
  Downloading pyparsing-3.0.6-py3-none-any.whl (97 kB)
Collecting six>=1.5
  Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: pip, wheel, setuptools, kiwisolver, fonttools, pyparsing, packaging, cycler, numpy, pillow, six, python-dateutil, matplotlib
  Attempting uninstall: pip
    Found existing installation: pip 20.0.2
    Uninstalling pip-20.0.2:
      Successfully uninstalled pip-20.0.2
  Attempting uninstall: setuptools
    Found existing installation: setuptools 44.0.0
    Uninstalling setuptools-44.0.0:
      Successfully uninstalled setuptools-44.0.0
Successfully installed cycler-0.11.0 fonttools-4.28.5 kiwisolver-1.3.2 matplotlib-3.5.1 numpy-1.22.0 packaging-21.3 pillow-9.0.0 pip-21.3.1 pyparsing-3.0.6 python-dateutil-2.8.2 setuptools-60.5.0 six-1.16.0 wheel-0.37.1
Virtual environment './venv' successfully created.

 *** caught segfault ***
address 0x7ffaeabe1100, cause 'memory not mapped'

Traceback:
 1: py_run_string_impl(code, local, convert)
 2: reticulate::py_run_string("import matplotlib.pyplot as plt; plt.plot([1, 2, 3], [1, 2, 3])")
An irrecoverable exception occurred. R is aborting now ...

Things I have noted:

A minimal example inovling eg. the pandas package, rather than matplotlib, runs successfully. ie. if test.R contains:

reticulate::virtualenv_create(
  envname = "./venv",
  packages = c("pandas")
)

reticulate::use_virtualenv("./venv")

reticulate::py_run_string("import pandas as pd; df = pd.DataFrame()")

If you enter the container interactively (docker run -it segfault-reprex /bin/bash), run test.R (Rscript /root/test.R), activate the resulting virutalenv (source /root/venv/bin/activate), you can use matplotlib fine from python (python -c "import matplotlib.pyplot as plt; plt.plot([1, 2, 3], [1, 2, 3])")

The reticulate documentation states that:

for reticulate to bind to a version of Python it must be compiled with shared library support (i.e. with the --enable-shared flag)

docker run -it segfault-reprex /usr/bin/python3 -c "import sysconfig; print(sysconfig.get_config_vars('Py_ENABLE_SHARED'))" shows that the container's Python was compiled with shared library support

ANSWER

Answered 2022-Jan-26 at 16:45

The problem is that the R binary in rocker/r-ver:latest is compiled against a different BLAS library to the one which the numpy on PyPI is compiled against.

This was explained to me by Tomasz Kalinowski here.

The solution is to ensure numpy uses the same BLAS libraries as rocker/r-ver's R binary does. An easy way to ensure this is to compile numpy from source. This compilation could be performed at either image build-time or container runtime.

Compiling numpy at runtime

To compile numpy at container runtime we can leave our Dockerfile as is, and add a call to system2() after our initial call to reticulate::virtualenv_create(). Altering test.R to become:

reticulate::virtualenv_create(
  envname = "./venv",
  packages = c("matplotlib")
)

system2("./venv/bin/pip3", c("install",
                             "--no-binary='numpy'",
                             "numpy",
                             "--ignore-installed"))

reticulate::use_virtualenv("./venv")

reticulate::py_run_string("import matplotlib.pyplot as plt;plt.plot([1, 2, 3], [1, 2, 3])")

After rebuilding our image, we can run test.R in this container without segfault!

Compiling numpy at build-time

Compiling numpy at runtime adds ~3 mins to every call of our R script!

A better solution could be to perform this compilation at image build-time. This would mean we'd only have to wait those ~3 minutes once (at image build time), rather than every time we run our script!

A Dockerfile to do so could look like:

FROM rocker/r-ver:latest

RUN apt update && apt install -y python3 python3-dev python3-venv

RUN install2.r --error reticulate

# Create a venv
RUN python3 -m venv /root/venv

# Compile numpy from source into venv
RUN /root/venv/bin/pip3 install --no-binary="numpy" numpy --ignore-installed

COPY test.R /root/

The accompanying test.R file would then make use of reticulate::virtualenv_install() as:

reticulate::virtualenv_install(
  envname = "/root/venv",
  packages = c("matplotlib")
)

reticulate::use_virtualenv("/root/venv")

reticulate::py_run_string("import matplotlib.pyplot as plt;plt.plot([1, 2, 3], [1, 2, 3])")

NB. when running a container from the image with numpy already compiled, you'll need to run as either root (-u="root"), or else change the permissions on the compiled numpy version in the Dockerfile; otherwise you will encounter a permissions error.

Source https://stackoverflow.com/questions/70711946

QUESTION

django rest Error - AttributeError: module 'collections' has no attribute 'MutableMapping'

Asked 2022-Jan-07 at 19:13

I'm build Django app, and it's work fine on my machine, but when I run inside docker container it's rest framework keep crashing, but when I comment any connection with rest framework it's work fine.

My machine: Kali Linux 2021.3
docker machine: Raspberry Pi 4 4gb
docker container image: python:rc-alpine3.14
python version on my machine: Python 3.9.7
python version on container: Python 3.10.0rc2

error output:

Traceback (most recent call last):
  File "/app/manage.py", line 22, in 
    main()
  File "/app/manage.py", line 18, in main
    execute_from_command_line(sys.argv)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/__init__.py", line 419, in execute_from_command_line
    utility.execute()
  File "/usr/local/lib/python3.10/site-packages/django/core/management/__init__.py", line 413, in execute
    self.fetch_command(subcommand).run_from_argv(self.argv)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/commands/test.py", line 23, in run_from_argv
    super().run_from_argv(argv)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/base.py", line 354, in run_from_argv
    self.execute(*args, **cmd_options)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/base.py", line 398, in execute
    output = self.handle(*args, **options)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/commands/test.py", line 55, in handle
    failures = test_runner.run_tests(test_labels)
  File "/usr/local/lib/python3.10/site-packages/django/test/runner.py", line 728, in run_tests
    self.run_checks(databases)
  File "/usr/local/lib/python3.10/site-packages/django/test/runner.py", line 665, in run_checks
    call_command('check', verbosity=self.verbosity, databases=databases)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/__init__.py", line 181, in call_command
    return command.execute(*args, **defaults)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/base.py", line 398, in execute
    output = self.handle(*args, **options)
  File "/usr/local/lib/python3.10/site-packages/django/core/management/commands/check.py", line 63, in handle
    self.check(
  File "/usr/local/lib/python3.10/site-packages/django/core/management/base.py", line 419, in check
    all_issues = checks.run_checks(
  File "/usr/local/lib/python3.10/site-packages/django/core/checks/registry.py", line 76, in run_checks
    new_errors = check(app_configs=app_configs, databases=databases)
  File "/usr/local/lib/python3.10/site-packages/django/core/checks/urls.py", line 13, in check_url_config
    return check_resolver(resolver)
  File "/usr/local/lib/python3.10/site-packages/django/core/checks/urls.py", line 23, in check_resolver
    return check_method()
  File "/usr/local/lib/python3.10/site-packages/django/urls/resolvers.py", line 412, in check
    for pattern in self.url_patterns:
  File "/usr/local/lib/python3.10/site-packages/django/utils/functional.py", line 48, in __get__
    res = instance.__dict__[self.name] = self.func(instance)
  File "/usr/local/lib/python3.10/site-packages/django/urls/resolvers.py", line 598, in url_patterns
    patterns = getattr(self.urlconf_module, "urlpatterns", self.urlconf_module)
  File "/usr/local/lib/python3.10/site-packages/django/utils/functional.py", line 48, in __get__
    res = instance.__dict__[self.name] = self.func(instance)
  File "/usr/local/lib/python3.10/site-packages/django/urls/resolvers.py", line 591, in urlconf_module
    return import_module(self.urlconf_name)
  File "/usr/local/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "", line 1050, in _gcd_import
  File "", line 1027, in _find_and_load
  File "", line 1006, in _find_and_load_unlocked
  File "", line 688, in _load_unlocked
  File "", line 883, in exec_module
  File "", line 241, in _call_with_frames_removed
  File "/app/project/urls.py", line 24, in 
    path("", include("apps.urls", namespace="apps")),
  File "/usr/local/lib/python3.10/site-packages/django/urls/conf.py", line 34, in include
    urlconf_module = import_module(urlconf_module)
  File "/usr/local/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "", line 1050, in _gcd_import
  File "", line 1027, in _find_and_load
  File "", line 1006, in _find_and_load_unlocked
  File "", line 688, in _load_unlocked
  File "", line 883, in exec_module
  File "", line 241, in _call_with_frames_removed
  File "/app/apps/urls.py", line 3, in 
    from . import api
  File "/app/apps/api.py", line 1, in 
    from .serializers import *
  File "/app/apps/serializers.py", line 1, in 
    from rest_framework import serializers
  File "/usr/local/lib/python3.10/site-packages/rest_framework/serializers.py", line 27, in 
    from rest_framework.compat import postgres_fields
  File "/usr/local/lib/python3.10/site-packages/rest_framework/compat.py", line 59, in 
    import requests
  File "/usr/local/lib/python3.10/site-packages/requests/__init__.py", line 63, in 
    from . import utils
  File "/usr/local/lib/python3.10/site-packages/requests/utils.py", line 27, in 
    from .cookies import RequestsCookieJar, cookiejar_from_dict
  File "/usr/local/lib/python3.10/site-packages/requests/cookies.py", line 172, in 
    class RequestsCookieJar(cookielib.CookieJar, collections.MutableMapping):
AttributeError: module 'collections' has no attribute 'MutableMapping'

Dockerfile

FROM python:rc-alpine3.14

COPY . /app 
WORKDIR /app

ENV UWSGI_PROFILE=core
ENV PYTHONUNBUFFERED=TRUE
RUN apk add --update --no-cache g++ gcc libxslt-dev # add some nessery libs
RUN apk add python3-dev build-base linux-headers pcre-dev
RUN pip3 install -U pip # upgrade pip
RUN apk update \
    && apk add --virtual build-deps gcc python3-dev musl-dev \
    && apk add jpeg-dev zlib-dev libjpeg \
    && pip install Pillow \
    && apk del build-deps


RUN apk add --no-cache --virtual=build-dependencies wget ca-certificates && \
    wget "https://bootstrap.pypa.io/get-pip.py" -O /dev/stdout | python


RUN pip install uwsgi

RUN pip3 install -r requirements.txt # install all requirements
RUN python uploadstatics.py

EXPOSE 80
CMD ["gunicorn","project.wsgi:application", "-b 0.0.0.0:8060"]

NOTES:

I use gunicorn to run the app (the error show even I run't from manage.py)

ANSWER

Answered 2022-Jan-07 at 19:13

You can downgrade your Python version. That should solve your problem; if not, use collections.abc.Mapping instead of the deprecated collections.Mapping.

Refer here: Link

Source https://stackoverflow.com/questions/69464512

QUESTION

Can't deploy streamlit app on share.streamlit.io

Asked 2021-Dec-25 at 14:42

I am working with a simple ML model with streamlit. It runs fine on my local machine inside conda environment, but it shows Error installing requirements when I try to deploy it on share.streamlit.io.
The error message is the following:

ERROR: Could not find a version that satisfies the requirement pywin32==303 (from versions: none)

ERROR: No matching distribution found for pywin32==303

This is the requirements.txt file for my model:

altair==4.1.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
astor==0.8.1
attrs==21.2.0
backcall==0.2.0
base58==2.1.1
bleach==4.1.0
blinker==1.4
cachetools==5.0.0
certifi==2021.10.8
cffi==1.15.0
charset-normalizer==2.0.9
click==7.1.2
colorama==0.4.4
cycler==0.11.0
debugpy==1.5.1
decorator==5.1.0
defusedxml==0.7.1
entrypoints==0.3
fonttools==4.28.5
gitdb==4.0.9
GitPython==3.1.24
idna==3.3
ipykernel==6.6.0
ipython==7.30.1
ipython-genutils==0.2.0
ipywidgets==7.6.5
jedi==0.18.1
Jinja2==3.0.3
joblib==1.1.0
jsonschema==4.3.2
jupyter-client==7.1.0
jupyter-core==4.9.1
jupyterlab-pygments==0.1.2
jupyterlab-widgets==1.0.2
kiwisolver==1.3.2
MarkupSafe==2.0.1
matplotlib==3.5.1
matplotlib-inline==0.1.3
mistune==0.8.4
nbclient==0.5.9
nbconvert==6.3.0
nbformat==5.1.3
nest-asyncio==1.5.4
notebook==6.4.6
numpy==1.21.5
packaging==21.3
pandas==1.3.5
pandocfilters==1.5.0
parso==0.8.3
pickleshare==0.7.5
Pillow==8.4.0
prometheus-client==0.12.0
prompt-toolkit==3.0.24
protobuf==3.19.1
pyarrow==6.0.1
pycparser==2.21
pydeck==0.7.1
Pygments==2.10.0
Pympler==1.0.1
pyparsing==3.0.6
pyrsistent==0.18.0
python-dateutil==2.8.2
pytz==2021.3
pytz-deprecation-shim==0.1.0.post0
pywin32==303
pywinpty==1.1.6
pyzmq==22.3.0
requests==2.26.0
scikit-learn==1.0.1
scipy==1.7.3
seaborn==0.11.2
Send2Trash==1.8.0
six==1.16.0
smmap==5.0.0
streamlit==1.3.0
terminado==0.12.1
testpath==0.5.0
threadpoolctl==3.0.0
toml==0.10.2
toolz==0.11.2
tornado==6.1
traitlets==5.1.1
typing_extensions==4.0.1
tzdata==2021.5
tzlocal==4.1
urllib3==1.26.7
validators==0.18.2
watchdog==2.1.6
wcwidth==0.2.5
webencodings==0.5.1
widgetsnbextension==3.5.2
wincertstore==0.2

What should I do to resolve this error?

ANSWER

Answered 2021-Dec-25 at 14:42

Streamlit share runs the app in a linux environment meaning there is no pywin32 because this is for windows.

Delete the pywin32 from the requirements file and also the pywinpty==1.1.6 for the same reason.

After deleting these requirements re-deploy your app and it will work.

Source https://stackoverflow.com/questions/70480314

QUESTION

Could not find a version that satisfies the requirement psycopg2

Asked 2021-Dec-05 at 21:00

I'm working on CI for my Python + Django project. I have to use the python:3.9-alpine image. A weird error is popping in my CI pipelines:

WARNING: Discarding https://files.pythonhosted.org/packages/aa/8a/7c80e7e44fb1b4277e89bd9ca509aefdd4dd1b2c547c6f293afe9f7ffd04/psycopg2-2.9.1.tar.gz#sha256=de5303a6f1d0a7a34b9d40e4d3bef684ccc44a49bbe3eb85e3c0bffb4a131b7c (from https://pypi.org/simple/psycopg2/) (requires-python:>=3.6). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
ERROR: Could not find a version that satisfies the requirement psycopg2==2.9.1 (from versions: 2.0.10, 2.0.11, 2.0.12, 2.0.13, 2.0.14, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.4.5, 2.4.6, 2.5, 2.5.1, 2.5.2, 2.5.3, 2.5.4, 2.5.5, 2.6, 2.6.1, 2.6.2, 2.7, 2.7.1, 2.7.2, 2.7.3, 2.7.3.1, 2.7.3.2, 2.7.4, 2.7.5, 2.7.6, 2.7.6.1, 2.7.7, 2.8, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.8.5, 2.8.6, 2.9, 2.9.1, 2.9.2)
  Preparing metadata (setup.py): finished with status 'error'
ERROR: No matching distribution found for psycopg2==2.9.1

I see 2.9.1 in list of avaliable versions

My .gitlab-ci.yml

stages:
  - linter
  - build_pip
  - build
  - meta
  - code_quality
  - deploy

.except-tags:
  except:
    - tags

build_pip:build_dist:
  stage: build_pip
  # image: $CI_DEPENDENCY_PROXY_GROUP_IMAGE_PREFIX/python:3.9-alpine
  image: python:3.9-alpine
  variables:
    OPENCV_VERSION: "4.5.3.56"
  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update apk add -q --update --no-cache
      - postgresql-dev musl-dev
      ...
    - pip install -r requirements/production.txt --no-cache
  script:
    - python setup.py bdist_wheel
    - echo PIP_CI_JOB_ID=$CI_JOB_ID > PIP_CI_JOB_ID.env
  dependencies: []
  artifacts:
    expire_in: 1 hour
    paths:
      - dist/
      - version
    reports:
      dotenv: PIP_CI_JOB_ID.env
  extends:
    - .except-tags
    ...

requirements/production.txt

djangorestframework==3.12.4
drf-extra-fields==3.1.1
djangorestframework-camel-case==1.2.0  # https://pypi.org/project/djangorestframework-camel-case/
Pillow==8.3.2
python-dateutil==2.8.2  # datetime formatting
psycopg2==2.9.1
opencv-python==4.5.3.56
drf-yasg==1.20.0
sentry-sdk==1.4.3
gunicorn==20.1.0
requests==2.26.0
yarl==1.7.0
googlemaps==4.5.3
django_redis==5.0.0
celery==5.2.0
channels==3.0.4
channels_redis==3.3.1

Full gitlab ci log: https://pastebin.com/QhMhErF7

What is the reason for this error?

I tried to replace psycopg2 with psycopg2-binary but the same error occours.

ANSWER

Answered 2021-Dec-05 at 17:35

What is the reason of my error?

Did you read my previous answer to a similar question of yours? The last part warns about certain combinations of Alpine + Python and this seems to be happening right now.

I tried to replace psycopg2 with psycopg2-binary but have the same error

The problem here might be a python library that has dependencies on gcc, which is not shipped on alpine by default.

Try replacing this:

  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --update --no-cache postgresql-dev musl-dev

with:

  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --no-cache postgresql-dev gcc python3-dev musl-dev

Notice that adding gcc will increase the image size, since this might be a dependency for either psycopg2 or psycopg2-binary. If the image size grows a lot I see no point in sticking with alpine, you could just avoid more Python headaches by switching to a debian-based image.

Source https://stackoverflow.com/questions/70235791

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Pillow

You can install using 'pip install Pillow' or download it from GitHub, PyPI.
You can use Pillow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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