vision | Datasets , Transforms and Models specific to Computer Vision | Computer Vision library
kandi X-RAY | vision Summary
kandi X-RAY | vision Summary
Datasets, Transforms and Models specific to Computer Vision
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Top functions reviewed by kandi - BETA
- Return a list of all supported extensions
- Create a feature extractor .
- Draws affine transformation .
- Uses SSDL Lite320 .
- Wrapper for FasterRCNN .
- Generate a grid of tensors .
- Read video from memory .
- Construct an SSD 2000 model .
- Construct a KeypointRCNN .
- Apply a transformation to an image .
vision Key Features
vision Examples and Code Snippets
Lines of Code : 37License : Permissive (Apache-2.0)
Copy@article{chen2022context,
title={Context autoencoder for self-supervised representation learning},
author={Chen, Xiaokang and Ding, Mingyu and Wang, Xiaodi and Xin, Ying and Mo, Shentong and Wang, Yunhao and Han, Shumin and Luo, Ping and Zeng, Ga import timm
model = timm.create_model('vit_base_patch16_224', pretrained=True)
model.eval()
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
config = resolve_dat import torch
from vit_pytorch.vit_for_small_dataset import ViT
v = ViT(
image_size = 256,
patch_size = 16,
num_classes = 1000,
dim = 1024,
depth = 6,
heads = 16,
mlp_dim = 2048,
dropout = 0.1,
emb_dropout = 0.1
)
name: temp_env
channels:
- pytorch
- conda-forge
dependencies:
- python=3.7
- pytorch::pytorch=1.11.0
- pytorch::torchvision=0.12.0
- pytorch::cpuonly
- pip>=22.0.4
- pip:
- -e '.[dev]'
from typing import Tuple, List, Dict, Optional
import torch
from torch import Tensor
from collections import OrderedDict
from torchvision.models.detection.roi_heads import fastrcnn_loss
from torchvision.models.detection.rpn import concat_b# list all ViT models
timm.list_models('vit_*')
# list all convNext models
timm.list_models('convnext*')
# load ViT-B/16
vit_b_16 = timm.create_model('vit_base_patch16_224', pretrained=True)
# load conv next
convnext = timm.create_model('name: neucon
channels:
# You can use the TUNA mirror to speed up the installation if you are in mainland China.
# - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
- pytorch
- defaults
- conda-forge
dependencies:
- Precision: TP / (TP + FP)
Recall: TP / (TP + FN)
F1: 2*Precision*Recall /(Precision + Recall)
def iou(self,a,b):
"""
Description
-----------
Calculates intersection over union for all setsmodel = torchvision.models.densenet201(num_classes=10)
params = model.state_dict()
name = 'features.norm0.num_batches_tracked'
print(id(params[name])) # 140247785908560
params[name] = params[name] + 0.1
print(id(params[name])) # 1402477te_data = np.ones([100, 32, 32, 3])
te_targets = np.ones([100])
assert all(tensors[0].shape[0] == tensor.shape[0] for tensor in tensors)
Community Discussions
Trending Discussions on vision
QUESTION
I tried a camera calibration with python and opencv to find the camera matrix. I used the following code from this link
https://automaticaddison.com/how-to-perform-camera-calibration-using-opencv/
...ANSWER
Answered 2021-Sep-13 at 11:31Your misconception is about "focal length". It's an overloaded term.
- "focal length" (unit mm) in the optical part: it describes the distance between the lens plane and image/sensor plane
- "focal length" (unit pixels) in the camera matrix: it describes a scale factor for mapping the real world to a picture of a certain resolution
1750 may very well be correct, if you have a high resolution picture (Full HD or something).
The calculation goes:
f [pixels] = (focal length [mm]) / (pixel pitch [µm / pixel])
(take care of the units and prefixes, 1 mm = 1000 µm)
Example: a Pixel 4a phone, which has 1.40 µm pixel pitch and 4.38 mm focal length, has f = ~3128.57 (= fx = fy).
Another example: A Pixel 4a has a diagonal Field of View of approximately 77.7 degrees, and a resolution of 4032 x 3024 pixels, so that's 5040 pixels diagonally. You can calculate:
f = (5040 / 2) / tan(~77.7° / 2)
f = ~3128.6 [pixels]
And that calculation you can apply to arbitrary cameras for which you know the field of view and picture size. Use horizontal FoV and horizontal resolution if the diagonal resolution is ambiguous. That can happen if the sensor isn't 16:9 but the video you take from it is cropped to 16:9... assuming the crop only crops vertically, and leaves the horizontal alone.
Why don't you need the size of the chessboard squares in this code? Because it only calibrates the intrinsic parameters (camera matrix and distortion coefficients). Those don't depend on the distance to the board or any other object in the scene.
If you were to calibrate extrinsic parameters, i.e. the distance of cameras in a stereo setup, then you would need to give the size of the squares.
QUESTION
i am trying to use 'normal' camera on my iphone 11 pro. I use react-native-vision-camera. When i run this code:
...ANSWER
Answered 2022-Mar-07 at 07:02tl;dr - Single-lens smartphone cameras commonly have a wide-angle lens of roughly 22mm and 30mm equivalent. So basically, you would want to choose wide-angle, as this is the "normal" type.
based on the react-native documentation, there are three Identifiers for a physical camera (one that exists on the back/front of the device):
"ultra-wide-angle-camera" | "wide-angle-camera" | "telephoto-camera"
"ultra-wide-angle-camera": A built-in camera with a shorter focal length than that of a wide-angle camera. (focal length between below 24mm)
"wide-angle-camera": A built-in wide-angle camera. (focal length between 24mm and 35mm)
"telephoto-camera": A built-in camera device with a longer focal length than a wide-angle camera. (focal length between above 85mm)
now that we have that settled, let's take a look at cameras' focal lengths that are equivalent to phone cameras' focal length (resource)
Camera type Focal length Angle-of-view Wide-angle 22mm to 30mm ~84° to ~62° Telephoto 50mm to 80mm ~40° to ~25° Ultrawide-angle 12mm to 18mm ~112° to ~90° Periscope 103mm to 125mm ~20° to ~16°what is considered a "normal" focal length is 35mm, so you should choose wide-angle since it is the closest (and eventually with the angle of view the user might be even closer to 35mm), further more the wide-angle is the most common focal-length for phone camera lens
QUESTION
Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!
List of bigrams:
...ANSWER
Answered 2022-Feb-16 at 20:28You could use a regex and extractall:
QUESTION
I want to remove the background, and draw the outline of the box shown in the image(there are multiple such images with a similar background) . I tried multiple methods in OpenCV, however I am unable to determine the combination of features which can help remove background for this image. Some of the approaches tried out were:
- Edge Detection - Since the background itself has edges of its own, using edge detection on its own(such as Canny and Sobel) didnt seem to give good results.
- Channel Filtering / Thresholding - Both the background and foreground have a similar white color, so I was unable to find a correct threshold to filter the foreground.
- Contour Detection - Since the background itself has a lot of contours, just using the largest contour area, as is often used for background removal, also didnt work.
I would be open to tools in Computer Vision or of Deep Learning (in Python) to solve this particular problem.
...ANSWER
Answered 2022-Jan-07 at 01:57This is one of the cases where it is really useful to fine-tune the kernels of which you are using to dilate and erode the canny edges detected from the images. Here is an example, where the dilation kernel is np.ones((4, 2)) and the erosion kernel is np.ones((13, 7)):
QUESTION
So today I updated Android Studio to:
...ANSWER
Answered 2021-Jul-30 at 07:00Encountered the same problem. Update Huawei services. Please take care. Remember to keep your dependencies on the most up-to-date version. This problem is happening on Merged-Manifest.
QUESTION
I am trying to write an object detection + text-to-speech code to detect objects and produce a voice output on the raspberry pi 4. However, as of right now, I am trying to write a simple python script that incorporates both elements into a single .py file and preferably as a function. I will then run this script on the raspberry pi. I want to give credit to Murtaza's Workshop "Object Detection OpenCV Python | Easy and Fast (2020)" and https://pypi.org/project/pyttsx3/ for the Text to speech documentation for pyttsx3. I have attached the code below. I have tried running the program and I always keep getting errors with the Text to speech code (commented lines 33-36 for reference). I believe it is some looping error but I just can't seem to get the program to run continuously. For instance, if I run the code without the TTS part, it works fine. Otherwise, it runs for perhaps 3-5 seconds and suddenly stops. I am a beginner but highly passionate in computer vision, and any help is appreciated!
...ANSWER
Answered 2021-Dec-28 at 16:46I installed pyttsx3 using the two commands in the terminal on the Raspberry Pi:
- sudo apt update && sudo apt install espeak ffmpeg libespeak1
- pip install pyttsx3
I followed the video youtube.com/watch?v=AWhDDl-7Iis&ab_channel=AiPhile to install pyttsx3. My functional code should also be listed above. My question should be resolved but hopefully useful to anyone looking to write a similar program. I have made minor tweaks to my code.
QUESTION
I'm using Huawei image segmentation for background removal from images. This code work perfectly fine on debug build but it does not work on a release build. I don't understand what could be the case.
Code:
...ANSWER
Answered 2021-Dec-27 at 08:50Stuff like this usually happens when you have ProGuard enabled but not correctly configured. Make sure to add appropriate rules to proguard-rules.pro file to prevent it from obfuscating relevant classes.
Information about this is usually provided by the library developers. After a quick search I came up with this example. Sources seem to be documented well enough, so that it should not be a problem to find the correct settings.
Keep in mind that you probably need to add rules for more than one library.
QUESTION
Im trying to filter posts by categories from this array
...ANSWER
Answered 2021-Dec-16 at 09:19You are getting the undefined error because for few of the cases the post_categories array is empty and if u try accessing the 0th element it will throw an error. So add a null check for the array length and for id something like below
QUESTION
Apple's sample code Identifying Trajectories in Video contains the following delegate callback:
...ANSWER
Answered 2021-Dec-12 at 17:03By the time you identify a trajectory in captured video frames or from frames decoded from a file you may not have the initial frames in memory any more, so the easiest way to create your file containing only trajectories is to keep the original file on hand, and then insert its trajectory snippets into an AVComposition which you then export using AVAssetExportSession.
This sample captures frames from the camera, encodes them to a file whilst analysing them for trajectories and after 20 seconds, it closes the file and then creates the new file containing only trajectory snippets.
If you're interested in detecting trajectories in a pre-existing file, it's not too hard to rewire this code.
QUESTION
So, I am building a prototype android app as an internship project for a startup in React Native v0.66. I was new to RN but not React when I set up the project. My choice for navigation fell upon React Navigation 6.x and their Native Stack Navigator because it performs better than the regular Stack Navigator, although is not as customizable according to docs.
Now I want to use react-native-gesture-handler in my project. According to their docs,
"If you are using a native navigation library like wix/react-native-navigation you need to follow a different setup for your Android app to work properly. The reason is that both native navigation libraries and Gesture Handler library need to use their own special subclasses of ReactRootView.
Instead of changing Java code you will need to wrap every screen component using gestureHandlerRootHOC on the JS side. This can be done for example at the stage when you register your screens."
I suppose this includes React Navigation-Native Stack Navigator as well? There is code example of how to implement RNGH with wix/react-native-navigation, but none, anywhere, for my case:
...ANSWER
Answered 2021-Nov-30 at 08:25I simply went with:
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Install vision
You can use vision 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.
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