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OpenCV numpy to cv::Mat conversion
Padding scipy affine_transform output to show non-overlapping regions of transformed images
Ignoring numbers in OpenCV corner detection
Remove horizontal lines with Open CV
Unknown OpenCV exception while using EasyOcr
Colab: (0) UNIMPLEMENTED: DNN library is not found
ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects
Finding straight lines from tightly coupled lines and noise curvy lines
Circular objects rotate angle detection
Could not find a version that satisfies the requirement psycopg2

Trending Discussions on opencv

QUESTION

OpenCV numpy to cv::Mat conversion

Asked 2022-Apr-05 at 12:36

I inherited an application with opencv, shiboken and pyside and my first task was to update to qt6, pyside6 and opencv 4.5.5. This has gone well so far, I can import the module and make class instances etc. However I have a crash when passing numpy arrays:

I am passing images in the form of numpy arrays through python to opencv and I am using pyopencv_to to convert from the array to cv::Mat. This worked in a previous version of opencv (4.5.3), but with 4.5.5 it seems to be broken.

When I try to pass an array through pyopencv_to, I get the exception opencv_ARRAY_API was nullptr. My predecessor solved this by directly calling PyInit_cv2(), which was apparently previously included via a header. But I cannot find any header in the git under the tag 4.5.3 that defines this function. Is this a file that is generated? I can see there is a pycompat.hpp, but that does not include the function either.

Is there a canonical way to initialize everything so that numpy arrays can be passed properly? Or a tutorial anyone can point me to? My searches have so far not produced any useful hints.

Thanks a lot in advance! :)

ANSWER

Answered 2022-Apr-05 at 12:36

I finally found a solution. I dont know if this is the correct way of doing it, but it works.

I made a header file that contains

PyMODINIT_FUNC PyInit_cv2();

as a forward declaration and then copied over everything in the modules/python/src2 directory. I assumed this was already happening in the cv2.cpp file, because there is already exactly that line (in cv2.cpp).

But just adding that include works perfectly fine, apparently. Now I can call the init function when my own module is initialized and it seems to properly set all the needed state.

Source https://stackoverflow.com/questions/71690714

QUESTION

Padding scipy affine_transform output to show non-overlapping regions of transformed images

Asked 2022-Mar-28 at 11:54

I have source (src) image(s) I wish to align to a destination (dst) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).

I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to scipy.ndimage.interpolate.affine_transform to recover the dst-aligned src image.

The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy's implementation.

Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of scipy's affine transformation, but I have as yet been unable to crack my particular needs.

The transformations from src to dst can have translations and rotation. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape argument in scipy.ndimage.interpolation.rotate). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct offset (see this question's answers again), but I can't get it working in all scenarios.

Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:

from scipy.ndimage import rotate, affine_transform
import numpy as np
import matplotlib.pyplot as plt

nblob = 50
shape = (200, 100)
buffered_shape = (300, 200)  # buffer for rotation and translation


def affine_test(angle=0, translate=(0, 0)):
    np.random.seed(42)
    # Maxiumum translation allowed is half difference between shape and buffered_shape

    # Generate a buffered_shape-sized base image with random blobs
    base = np.zeros(buffered_shape, dtype=np.float32)
    random_locs = np.random.choice(np.arange(2, buffered_shape[0] - 2), nblob * 2, replace=False)
    i = random_locs[:nblob]
    j = random_locs[nblob:]
    for k, (_i, _j) in enumerate(zip(i, j)):
        # Use different values, just to make it easier to distinguish blobs
        base[_i - 2 : _i + 2, _j - 2 : _j + 2] = k + 10

    # Impose a rotation and translation on source
    src = rotate(base, angle, reshape=False, order=1, mode="constant")
    bsc = (np.array(buffered_shape) / 2).astype(int)
    sc = (np.array(shape) / 2).astype(int)
    src = src[
        bsc[0] - sc[0] + translate[0] : bsc[0] + sc[0] + translate[0],
        bsc[1] - sc[1] + translate[1] : bsc[1] + sc[1] + translate[1],
    ]
    # Cut-out destination from the centre of the base image
    dst = base[bsc[0] - sc[0] : bsc[0] + sc[0], bsc[1] - sc[1] : bsc[1] + sc[1]]

    src_y, src_x = src.shape

    def get_matrix_offset(centre, angle, scale):
        """Follows OpenCV.getRotationMatrix2D"""
        angle = angle * np.pi / 180
        alpha = scale * np.cos(angle)
        beta = scale * np.sin(angle)
        return (
            np.array([[alpha, beta], [-beta, alpha]]),
            np.array(
                [
                    (1 - alpha) * centre[0] - beta * centre[1],
                    beta * centre[0] + (1 - alpha) * centre[1],
                ]
            ),
        )
    # Obtain the rotation matrix and offset that describes the transformation
    # between src and dst
    matrix, offset = get_matrix_offset(np.array([src_y / 2, src_x / 2]), angle, 1)
    offset = offset - translate

    # Determine the outer bounds of the new image
    lin_pts = np.array([[0, src_x, src_x, 0], [0, 0, src_y, src_y]])
    transf_lin_pts = np.dot(matrix.T, lin_pts) - offset[::-1].reshape(2, 1)

    # Find min and max bounds of the transformed image
    min_x = np.floor(np.min(transf_lin_pts[0])).astype(int)
    min_y = np.floor(np.min(transf_lin_pts[1])).astype(int)
    max_x = np.ceil(np.max(transf_lin_pts[0])).astype(int)
    max_y = np.ceil(np.max(transf_lin_pts[1])).astype(int)

    # Add translation to the transformation matrix to shift to positive values
    anchor_x, anchor_y = 0, 0
    if min_x < 0:
        anchor_x = -min_x
    if min_y < 0:
        anchor_y = -min_y
    shifted_offset = offset - np.dot(matrix, [anchor_y, anchor_x])

    # Create padded destination image
    dst_h, dst_w = dst.shape[:2]
    pad_widths = [anchor_y, max(max_y, dst_h) - dst_h, anchor_x, max(max_x, dst_w) - dst_w]
    dst_padded = np.pad(
        dst,
        ((pad_widths[0], pad_widths[1]), (pad_widths[2], pad_widths[3])),
        "constant",
        constant_values=-1,
    )
    dst_pad_h, dst_pad_w = dst_padded.shape

    # Create the aligned and padded source image
    source_aligned = affine_transform(
        src,
        matrix.T,
        offset=shifted_offset,
        output_shape=(dst_pad_h, dst_pad_w),
        order=3,
        mode="constant",
        cval=-1,
    )

    # Plot the images
    fig, axes = plt.subplots(1, 4, figsize=(10, 5), sharex=True, sharey=True)
    axes[0].imshow(src, cmap="viridis", vmin=-1, vmax=nblob)
    axes[0].set_title("Source")
    axes[1].imshow(dst, cmap="viridis", vmin=-1, vmax=nblob)
    axes[1].set_title("Dest")
    axes[2].imshow(source_aligned, cmap="viridis", vmin=-1, vmax=nblob)
    axes[2].set_title("Source aligned to Dest padded")
    axes[3].imshow(dst_padded, cmap="viridis", vmin=-1, vmax=nblob)
    axes[3].set_title("Dest padded")
    plt.show()

e.g.:

affine_test(0, (-20, 40))

gives:

With a zoom in showing the aligned in the padded images:

I require the full extent of the src and dst images aligned on the same pixel coordinates, with both rotations and translations.

Any help is greatly appreciated!

ANSWER

Answered 2022-Mar-22 at 16:44

If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :

from scipy.ndimage import rotate, shift

You need to find first the difference of angle between the two images angle_to_rotate, having that you apply a rotation to src:

angle_to_rotate = 25
rotated_src = rotate(src, angle_to_rotate , reshape=True, order=1, mode="constant")

With reshape=True you avoid losing information from your original src matrix, and it pads the result so the image could be translated around the 0,0 indexes. You can calculate this translation as it is (x*cos(angle),y*sin(angle) where x and y are the dimensions of the image, but it probably won't matter.

Now you will need to translate the image to the source, for doing that you can use the shift function:

rot_translated_src = shift(rotated_src , [distance_x, distance_y])

In this case there is no reshape (because otherwise you wouldn't have any real translation) so if the image was not previously padded some information will be lost.

But you can do some padding with

np.pad(src, number, mode='constant')

To calculate distance_x and distance_y you will need to find a point that serves you as a reference between the rotated_src and the destination, then just calculate the distance in the x and y axis.

Summary

Make some padding in src, and dst
Find the angular distance between them.
Rotate src with scipy.ndimage.rotate using reshape=True
Find the horizontal and vertical distance distance_x, distance_y between the rotated image and dst
Translate your 'rotated_src' with scipy.ndimage.shift

Code

from scipy.ndimage import rotate, shift
import matplotlib.pyplot as plt
import numpy as np

First we make the destination image:

# make and plot dest
dst = np.ones([40,20])
dst = np.pad(dst,10)
dst[17,[14,24]]=4
dst[27,14:25]=4
dst[26,[14,25]]=4
rotated_dst = rotate(dst, 20, order=1)

plt.imshow(dst) # plot it
plt.imshow(rotated_dst)
plt.show()

We make the Source image:

# make_src image and plot it
src = np.zeros([40,20])
src = np.pad(src,10)
src[0:20,0:20]=1
src[7,[4,14]]=4
src[17,4:15]=4
src[16,[4,15]]=4
plt.imshow(src)
plt.show()

Then we align the src to the destination:

rotated_src = rotate(src, 20, order=1) # find the angle 20, reshape true is by default
plt.imshow(rotated_src)
plt.show()
distance_y = 8 # find this distances from rotated_src and dst
distance_x = 12 # use any visual reference or even the corners
translated_src = shift(rotated_src, [distance_y,distance_x])
plt.imshow(translated_src)
plt.show()

pd: If you find problems to find the angle and the distances in a programmatic way, please leave a comment providing a bit more of insight of what can be used as a reference that could be for example the frame of the image or some image features / data)

Source https://stackoverflow.com/questions/71516584

QUESTION

Ignoring numbers in OpenCV corner detection

Asked 2022-Mar-28 at 00:09

In the image below, I am using opencv harris corner detector to detect only the corners for the squares (and the smaller squares within the outer squares). However, I am also getting corners detected for the numbers on the side of the image. How do I get this to focus only on the squares and not the numbers? The code, input image and output image are below:

import cv2 as cv
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv.cornerHarris(gray, 2, 3, 0.04)
dst = cv.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
cv.imshow('dst', img)

Input image

Output from Harris corner detector

ANSWER

Answered 2022-Mar-28 at 00:09

Here's a potential approach using traditional image processing:

Obtain binary image. We load the image, convert to grayscale, Gaussian blur, then adaptive threshold to obtain a black/white binary image. We then remove small noise using contour area filtering. At this stage we also create two blank masks.
Detect horizontal and vertical lines. Now we isolate horizontal lines by creating a horizontal shaped kernel and perform morphological operations. To detect vertical lines, we do the same but with a vertical shaped kernel. We draw the detected lines onto separate masks.
Find intersection points. The idea is that if we combine the horizontal and vertical masks, the intersection points will be the corners. We can perform a bitwise-and operation on the two masks. Finally we find the centroid of each intersection point and highlight corners by drawing a circle.

Here's a visualization of the pipeline

Input image -> binary image

Detected horizontal lines -> horizontal mask

Detected vertical lines -> vertical mask

Bitwise-and both masks -> detected intersection points -> corners -> cleaned up corners

The results aren't perfect but it's pretty close. The problem comes from the noise on the vertical mask due to the slanted image. If the image was centered without an angle, the results would be ideal. You can probably fine tune the kernel sizes or iterations to get better results.

Code

import cv2
import numpy as np

# Load image, create horizontal/vertical masks, Gaussian blur, Adaptive threshold
image = cv2.imread('1.png')
original = image.copy()
horizontal_mask = np.zeros(image.shape, dtype=np.uint8)
vertical_mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 23, 7)

# Remove small noise on thresholded image
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 150:
        cv2.drawContours(thresh, [c], -1, 0, -1)

# Detect horizontal lines
dilate_horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,1))
dilate_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_horizontal_kernel, iterations=1)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
detected_lines = cv2.morphologyEx(dilate_horizontal, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 2)
    cv2.drawContours(horizontal_mask, [c], -1, (255,255,255), 2)

# Remove extra horizontal lines using contour area filtering
horizontal_mask = cv2.cvtColor(horizontal_mask,cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(horizontal_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 1000 or area < 100:
        cv2.drawContours(horizontal_mask, [c], -1, 0, -1)

# Detect vertical 
dilate_vertical_kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (1,7))
dilate_vertical = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, dilate_vertical_kernel, iterations=1)
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,2))
detected_lines = cv2.morphologyEx(dilate_vertical, cv2.MORPH_OPEN, vertical_kernel, iterations=4)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 2)
    cv2.drawContours(vertical_mask, [c], -1, (255,255,255), 2)

# Find intersection points
vertical_mask = cv2.cvtColor(vertical_mask,cv2.COLOR_BGR2GRAY)
combined = cv2.bitwise_and(horizontal_mask, vertical_mask)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
combined = cv2.morphologyEx(combined, cv2.MORPH_OPEN, kernel, iterations=1)

# Highlight corners
cnts = cv2.findContours(combined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Find centroid and draw center point
    try:
        M = cv2.moments(c)
        cx = int(M['m10']/M['m00'])
        cy = int(M['m01']/M['m00'])
        cv2.circle(original, (cx, cy), 3, (36,255,12), -1)
    except ZeroDivisionError:
        pass

cv2.imshow('thresh', thresh)
cv2.imshow('horizontal_mask', horizontal_mask)
cv2.imshow('vertical_mask', vertical_mask)
cv2.imshow('combined', combined)
cv2.imshow('original', original)
cv2.imshow('image', image)
cv2.waitKey()

Source https://stackoverflow.com/questions/71596075

QUESTION

Remove horizontal lines with Open CV

Asked 2022-Mar-15 at 13:22

I am trying to remove horizontal lines from my daughter's drawings, but can't get it quite right.

The approach I am following is creating a mask with horizontal lines (https://stackoverflow.com/a/57410471/1873521) and then removing that mask from the original (https://docs.opencv.org/3.3.1/df/d3d/tutorial_py_inpainting.html).

As you can see in the pics below, this only partially removes the horizontal lines, and also creates a few distortions, as some of the original drawing horizontal-ish lines also end up in the mask.

Any help improving this approach would be greatly appreciated!

Create mask with horizontal lines

From https://stackoverflow.com/a/57410471/1873521

import cv2
import numpy as np

img = cv2.imread("input.png", 0)

if len(img.shape) != 2:
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
    gray = img

gray = cv2.bitwise_not(gray)
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, 
cv2.THRESH_BINARY, 15, -2)

horizontal = np.copy(bw)

cols = horizontal.shape[1]
horizontal_size = cols // 30

horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))

horizontal = cv2.erode(horizontal, horizontalStructure)
horizontal = cv2.dilate(horizontal, horizontalStructure)

cv2.imwrite("horizontal_lines_extracted.png", horizontal)

Remove horizontal lines using mask

From https://docs.opencv.org/3.3.1/df/d3d/tutorial_py_inpainting.html

import numpy as np
import cv2
mask = cv2.imread('horizontal_lines_extracted.png',0)
dst = cv2.inpaint(img,mask,3,cv2.INPAINT_TELEA)
cv2.imwrite("original_unmasked.png", dst)

Pics Original picture

Mask

Partially cleaned:

ANSWER

Answered 2022-Mar-14 at 16:58

Get the Edges
Dilate to close the lines
Hough line to detect the lines
Filter out the non horizontal lines
Inpaint the mask
Getting the Edges

gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize=3)

Dilate to close the lines

img_dilation = cv2.dilate(edges, np.ones((3,3), np.uint8), iterations=1)

Hough line to detect the lines

lines = cv2.HoughLinesP(
            img_dilation, # Input edge image
            1, # Distance resolution in pixels
            np.pi/180, # Angle resolution in radians
            threshold=100, # Min number of votes for valid line
            minLineLength=5, # Min allowed length of line
            maxLineGap=10 # Max allowed gap between line for joining them
            )

Filter out the non horizontal lines using slope.

lines_list = []

for points in lines:
    x1,y1,x2,y2=points[0]
    lines_list.append([(x1,y1),(x2,y2)])
    slope = ((y2-y1) / (x2-x1)) if (x2-x1) != 0 else np.inf
    
    if slope <= 1:
        cv2.line(mask,(x1,y1),(x2,y2), color=(255, 255, 255),thickness = 2)

Inpaint the mask

result = cv2.inpaint(image,mask,3,cv2.INPAINT_TELEA)

Full Code:

import cv2
import numpy as np
 
# Read image
image = cv2.imread('input.jpg')
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)

# Convert image to grayscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
 
# Use canny edge detection
edges = cv2.Canny(gray,50,150,apertureSize=3)

# Dilating
img_dilation = cv2.dilate(edges, np.ones((3,3), np.uint8), iterations=1)

 
# Apply HoughLinesP method to
# to directly obtain line end points
lines = cv2.HoughLinesP(
            img_dilation, # Input edge image
            1, # Distance resolution in pixels
            np.pi/180, # Angle resolution in radians
            threshold=100, # Min number of votes for valid line
            minLineLength=5, # Min allowed length of line
            maxLineGap=10 # Max allowed gap between line for joining them
            )

lines_list = []

for points in lines:
    x1,y1,x2,y2=points[0]
    lines_list.append([(x1,y1),(x2,y2)])
    slope = ((y2-y1) / (x2-x1)) if (x2-x1) != 0 else np.inf
    
    if slope <= 1:
        cv2.line(mask,(x1,y1),(x2,y2), color=(255, 255, 255),thickness = 2)
    
result = cv2.inpaint(image,mask,3,cv2.INPAINT_TELEA)

Source https://stackoverflow.com/questions/71425968

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)
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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)
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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)
Requirement already satisfied: chardet<4,>=3.0.2 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) (3.0.4)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.1.0)
Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (13.0.0)
Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.1.0)
Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.6.3)
Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.4.0)
Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.2.0)
Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.10.0.2)
Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.13.3)
Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.23.1)
Collecting tf-estimator-nightly==2.8.0.dev2021122109
  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)
     |████████████████████████████████| 462 kB 49.5 MB/s
Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.1.2)
Collecting tensorboard<2.9,>=2.8
  Downloading tensorboard-2.8.0-py3-none-any.whl (5.8 MB)
     |████████████████████████████████| 5.8 MB 41.2 MB/s
Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (2.0)
Collecting keras
  Downloading keras-2.8.0-py2.py3-none-any.whl (1.4 MB)
     |████████████████████████████████| 1.4 MB 41.2 MB/s
Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.3.0)
Collecting numpy>=1.15.4
  Downloading numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)
     |████████████████████████████████| 15.7 MB 41.4 MB/s
Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.0.0)
Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.43.0)
Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.37.1)
Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.5.2)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.6.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.0.1)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.4.6)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.8.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.3.6)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.3.1)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (4.10.1)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.7.0)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.2.0)
Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-model-optimization>=0.4.1->tf-models-official>=2.5.1->object-detection==0.1) (0.1.6)
Requirement already satisfied: crcmod<2.0,>=1.7 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (1.7)
Collecting fastavro<2,>=0.21.4
  Downloading fastavro-1.4.9-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
     |████████████████████████████████| 2.3 MB 38.1 MB/s
Requirement already satisfied: pyarrow<7.0.0,>=0.15.1 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (6.0.1)
Requirement already satisfied: pydot<2,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam->object-detection==0.1) (1.3.0)
Collecting proto-plus<2,>=1.7.1
  Downloading proto_plus-1.19.9-py3-none-any.whl (45 kB)
     |████████████████████████████████| 45 kB 3.2 MB/s
Collecting requests<3.0.0dev,>=2.18.0
  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
     |████████████████████████████████| 63 kB 1.8 MB/s
Collecting dill<0.3.2,>=0.3.1.1
  Downloading dill-0.3.1.1.tar.gz (151 kB)
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Collecting numpy>=1.15.4
  Downloading numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.3 MB)
     |████████████████████████████████| 15.3 MB 21.1 MB/s
Collecting orjson<4.0
  Downloading orjson-3.6.6-cp37-cp37m-manylinux_2_24_x86_64.whl (245 kB)
     |████████████████████████████████| 245 kB 53.2 MB/s
Collecting hdfs<3.0.0,>=2.1.0
  Downloading hdfs-2.6.0-py3-none-any.whl (33 kB)
Collecting pymongo<4.0.0,>=3.8.0
  Downloading pymongo-3.12.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (508 kB)
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Requirement already satisfied: docopt in /usr/local/lib/python3.7/dist-packages (from hdfs<3.0.0,>=2.1.0->apache-beam->object-detection==0.1) (0.6.2)
Collecting protobuf>=3.12.0
  Downloading protobuf-3.19.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)
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Requirement already satisfied: charset-normalizer~=2.0.0 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) (2.0.11)
Requirement already satisfied: opencv-python>=4.1.0.25 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (4.1.2.30)
Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (0.11.0)
Requirement already satisfied: kiwisolver>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (1.3.2)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle>=1.3.9->tf-models-official>=2.5.1->object-detection==0.1) (1.3)
Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu->tf-models-official>=2.5.1->object-detection==0.1) (2019.12.20)
Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu->tf-models-official>=2.5.1->object-detection==0.1) (0.8.9)
Collecting portalocker
  Downloading portalocker-2.3.2-py2.py3-none-any.whl (15 kB)
Collecting colorama
  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)
Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from seqeval->tf-models-official>=2.5.1->object-detection==0.1) (1.0.2)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official>=2.5.1->object-detection==0.1) (1.1.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official>=2.5.1->object-detection==0.1) (3.1.0)
Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons->tf-models-official>=2.5.1->object-detection==0.1) (2.7.1)
Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (2.3)
Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (0.16.0)
Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (21.4.0)
Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (5.4.0)
Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (1.6.0)
Collecting tensorflow-io-gcs-filesystem>=0.23.1
  Downloading tensorflow_io_gcs_filesystem-0.24.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.1 MB)
     |████████████████████████████████| 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

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 <deb name>

(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

Finding straight lines from tightly coupled lines and noise curvy lines

Asked 2022-Jan-17 at 20:48

I have this image for a treeline crop. I need to find the general direction in which the crop is aligned. I'm trying to get the Hough lines of the image, and then find the mode of distribution of angles.

I've been following this tutorialon crop lines, however in that one, the crop lines are sparse. Here they are densely pack, and after grayscaling, blurring, and using canny edge detection, this is what i get

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('drive/MyDrive/tree/sample.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gauss = cv2.GaussianBlur(gray, (3,3), 3)

plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.imshow(gauss)

gscale = cv2.Canny(gauss, 80, 140)
plt.subplot(1,2,2)
plt.imshow(gscale)
plt.show()

(Left side blurred image without canny, left one preprocessed with canny)

After that, I followed the tutorial and "skeletonized" the preprocessed image

size = np.size(gscale)

skel = np.zeros(gscale.shape, np.uint8)

ret, gscale = cv2.threshold(gscale, 128, 255,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
done = False

while not done:
  eroded = cv2.erode(gscale, element)
  temp = cv2.dilate(eroded, element)
  temp = cv2.subtract(gscale, temp)
  skel = cv2.bitwise_or(skel, temp)
  gscale = eroded.copy()
  
  zeros = size - cv2.countNonZero(gscale)
  if zeros==size:
    done = True

Giving me

As you can see, there are a bunch of curvy lines still. When using the HoughLines algorithm on it, there are 11k lines scattered everywhere

lines = cv2.HoughLinesP(skel,1,np.pi/180,130)
a,b,c = lines.shape
for i in range(a):
    rho = lines[i][0][0]
    theta = lines[i][0][1]    
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))
    cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2, cv2.LINE_AA)#showing the results:

plt.figure(figsize=(15,15))
plt.subplot(121)#OpenCV reads images as BGR, this corrects so it is displayed as RGB
plt.plot()
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) 
plt.title('Row Detection')
plt.xticks([])
plt.yticks([])
plt.subplot(122)
plt.plot()
plt.imshow(skel,cmap='gray')
plt.title('Skeletal Image')
plt.xticks([])
plt.yticks([])
plt.show()

I am a newbie when it comes to cv2, so I have 0 clue what to do. Searched and tried a bunch of stuff but none works. How can I remove the mildly big dots, and remove the squiggly lines?

ANSWER

Answered 2022-Jan-02 at 14:10

You can use a 2D FFT to find the general direction in which the crop is aligned (as proposed by mozway in the comments). The idea is that the general direction can be easily extracted from centred beaming rays appearing in the magnitude spectrum when the input contains many lines in the same direction. You can find more information about how it works in this previous post. It works directly with the input image, but it is better to apply the Gaussian + Canny filters.

Here is the interesting part of the magnitude spectrum of the filtered gray image:

The main beaming ray can be easily seen. You can extract its angle by iterating over many lines with an increasing angle and sum the magnitude values on each line as in the following figure:

Here is the magnitude sum of each line plotted against the angle (in radian) of the line:

Based on that, you just need to find the angle that maximize the computed sum.

Here is the resulting code:

def computeAngle(arr):
    # Naive inefficient algorithm
    n, m = arr.shape
    yCenter, xCenter = (n-1, m//2-1)
    lineLen = m//2-2
    sMax = 0.0
    bestAngle = np.nan
    for angle in np.arange(0, math.pi, math.pi/300):
        i = np.arange(lineLen)
        y, x = (np.sin(angle) * i + 0.5).astype(np.int_), (np.cos(angle) * i + 0.5).astype(np.int_)
        s = np.sum(arr[yCenter-y, xCenter+x])
        if s > sMax:
            bestAngle = angle
            sMax = s
    return bestAngle

# Load the image in gray
img = cv2.imread('lines.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# Apply some filters
gauss = cv2.GaussianBlur(gray, (3,3), 3)
gscale = cv2.Canny(gauss, 80, 140)

# Compute the 2D FFT of real values
freqs = np.fft.rfft2(gscale)

# Shift the frequencies (centering) and select the low frequencies
upperPart = freqs[:freqs.shape[0]//4,:freqs.shape[1]//2]
lowerPart = freqs[-freqs.shape[0]//4:,:freqs.shape[1]//2]
filteredFreqs = np.vstack((lowerPart, upperPart))

# Compute the magnitude spectrum
magnitude = np.log(np.abs(filteredFreqs))

# Correct the angle
magnitude = np.rot90(magnitude).copy()

# Find the major angle
bestAngle = computeAngle(magnitude)

Source https://stackoverflow.com/questions/70545797

QUESTION

Circular objects rotate angle detection

Asked 2022-Jan-04 at 05:52

I'm trying to detect angle difference between two circular objects, which be shown as 2 image below.

I'm thinking about rotate one of image with some small angle. Every time one image rotated, SSIM between rotated image and the another image will be calculated. The angle with maximum SSIM will be the angle difference.

But, finding the extremes is never an easy problem. So my question is: Are there another algorithms (opencv) can be used is this case?

IMAGE #1

IMAGE #2

EDIT:

Thanks @Micka, I just do the same way he suggest and remove black region like @Yves Daoust said to improve processing time. Here is my final result:

ORIGINAL IMAGE ROTATED + SHIFTED IMAGE

ANSWER

Answered 2021-Dec-15 at 09:19

Here's a way to do it:

detect circles (for the example I assume circle is in the image center and radius is 50% of the image width)
unroll circle images by polar coordinates
make sure that the second image is fully visible in the first image, without a "circle end overflow"
simple template matching

Result for the following code:

min: 9.54111e+07
pos: [0, 2470]
angle-right: 317.571
angle-left: -42.4286

I think this should work quite well in general.

int main()
{
    // load images
    cv::Mat image1 = cv::imread("C:/data/StackOverflow/circleAngle/circleAngle1.jpg");
    cv::Mat image2 = cv::imread("C:/data/StackOverflow/circleAngle/circleAngle2.jpg");

    // generate circle information. Here I assume image center and image is filled by the circles.
    // use houghCircles or a RANSAC based circle detection instead, if necessary
    cv::Point2f center1 = cv::Point2f(image1.cols/2.0f, image1.rows/2.0f);
    cv::Point2f center2 = cv::Point2f(image2.cols / 2.0f, image2.rows / 2.0f);
    float radius1 = image1.cols / 2.0f;
    float radius2 = image2.cols / 2.0f;

    cv::Mat unrolled1, unrolled2;
    // define a size for the unrolling. Best might be to choose the arc-length of the circle. The smaller you choose this, the less resolution is available (the more pixel information of the circle is lost during warping)
    cv::Size unrolledSize(radius1, image1.cols * 2);

    // unroll the circles by warpPolar
    cv::warpPolar(image1, unrolled1, unrolledSize, center1, radius1, cv::WARP_POLAR_LINEAR);
    cv::warpPolar(image2, unrolled2, unrolledSize, center2, radius2, cv::WARP_POLAR_LINEAR);

    // double the first image (720° of the circle), so that the second image is fully included without a "circle end overflow"
    cv::Mat doubleImg1;
    cv::vconcat(unrolled1, unrolled1, doubleImg1);

    // the height of the unrolled image is exactly 360° of the circle
    double degreesPerPixel = 360.0 / unrolledSize.height;

    // template matching. Maybe correlation could be the better matching metric
    cv::Mat matchingResult;
    cv::matchTemplate(doubleImg1, unrolled2, matchingResult, cv::TemplateMatchModes::TM_SQDIFF);

    double minVal; double maxVal; cv::Point minLoc; cv::Point maxLoc;
    cv::Point matchLoc;
    cv::minMaxLoc(matchingResult, &minVal, &maxVal, &minLoc, &maxLoc, cv::Mat());

    std::cout << "min: " << minVal << std::endl;
    std::cout << "pos: " << minLoc << std::endl;

    // angles in clockwise direction:
    std::cout << "angle-right: " << minLoc.y * degreesPerPixel << std::endl;
    std::cout << "angle-left: " << minLoc.y * degreesPerPixel -360.0 << std::endl;
    double foundAngle = minLoc.y * degreesPerPixel;
    
    // visualizations:
    // display the matched position
    cv::Rect pos = cv::Rect(minLoc, cv::Size(unrolled2.cols, unrolled2.rows));
    cv::rectangle(doubleImg1, pos, cv::Scalar(0, 255, 0), 4);

    // resize because the images are too big
    cv::Mat resizedResult;
    cv::resize(doubleImg1, resizedResult, cv::Size(), 0.2, 0.2);
    
    cv::resize(unrolled1, unrolled1, cv::Size(), 0.2, 0.2);
    cv::resize(unrolled2, unrolled2, cv::Size(), 0.2, 0.2);

    double startAngleUpright = 0;
    cv::ellipse(image1, center1, cv::Size(100, 100), 0, startAngleUpright, startAngleUpright + foundAngle, cv::Scalar::all(255), -1, 0);

    cv::resize(image1, image1, cv::Size(), 0.5, 0.5);
    cv::imshow("image1", image1);

    cv::imshow("unrolled1", unrolled1);
    cv::imshow("unrolled2", unrolled2);

    cv::imshow("resized", resizedResult);

    cv::waitKey(0);

    
}

This is how the intermediate images and results look like:

unrolled image 1 / unrolled 2 / unrolled 1 (720°) / best match of unrolled 2 in unrolled 1 (720°):

Source https://stackoverflow.com/questions/70359226

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

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