computer-vision

Clustering human faces from a video

丶灬走出姿态 提交于 2021-02-07 03:43:02
问题 I have run the face detection algorithm inbuilt in opencv to extract faces in each frame of a video(sampled at 1 fps). I have also resized each face image to be of same size and I have cropped some fraction of image to remove background noise and hair. Now the problem is that I have to cluster these images of faces - Each cluster corresponding to a person. I implemented the algorithm described here http://bitsearch.blogspot.in/2013/02/unsupervised-face-clustering-with-opencv.html Basically

How to segment blood vessels python opencv

大城市里の小女人 提交于 2021-02-06 10:54:51
问题 I am trying to segment the blood vessels in retinal images using Python and OpenCV. Here is the original image: Ideally I want all the blood vessels to be very visible like this (different image): Here is what I have tried so far. I took the green color channel of the image. img = cv2.imread('images/HealthyEyeFundus.jpg') b,g,r = cv2.split(img) Then I tried to create a matched filter by following this article and this is what the output image is: Then I tried doing max entropy thresholding:

How to segment blood vessels python opencv

ぐ巨炮叔叔 提交于 2021-02-06 10:53:18
问题 I am trying to segment the blood vessels in retinal images using Python and OpenCV. Here is the original image: Ideally I want all the blood vessels to be very visible like this (different image): Here is what I have tried so far. I took the green color channel of the image. img = cv2.imread('images/HealthyEyeFundus.jpg') b,g,r = cv2.split(img) Then I tried to create a matched filter by following this article and this is what the output image is: Then I tried doing max entropy thresholding:

Best strategy to reduce false positives: Google's new Object Detection API on Satellite Imagery

孤街浪徒 提交于 2021-02-05 13:43:18
问题 I'm setting up the new Tensorflow Object Detection API to find small objects in large areas of satellite imagery. It works quite well - it finds all 10 objects I want, but I also get 50-100 false positives [things that look a little like the target object, but aren't]. I'm using the sample config from the 'pets' tutorial, to fine-tune the faster_rcnn_resnet101_coco model they offer. I've started small, with only 100 training examples of my objects (just 1 class). 50 examples in my validation

Best strategy to reduce false positives: Google's new Object Detection API on Satellite Imagery

做~自己de王妃 提交于 2021-02-05 13:42:27
问题 I'm setting up the new Tensorflow Object Detection API to find small objects in large areas of satellite imagery. It works quite well - it finds all 10 objects I want, but I also get 50-100 false positives [things that look a little like the target object, but aren't]. I'm using the sample config from the 'pets' tutorial, to fine-tune the faster_rcnn_resnet101_coco model they offer. I've started small, with only 100 training examples of my objects (just 1 class). 50 examples in my validation

How to get the camera rotation? (Aruco Library)

我是研究僧i 提交于 2021-02-05 10:41:10
问题 I've been trying to understand the output of the aruco_test.cpp program that is included when you download the Aruco Library. The output has this format: 22=(236.87,86.4296) (422.581,78.3856) (418.21,228.032) (261.347,228.529) Txyz=0.00813142 -0.0148134 0.140595 Rxyz=-2.14032 0.0777095 0.138929 22 is the unique identifier of the marker, the next four pairs of numbers are the four corners of the marker. My problem here is the two vectors Tvec and Rvec. I've been reading on the Internet that

SimpleITK Resize images

[亡魂溺海] 提交于 2021-02-04 14:08:07
问题 I have a set o 3D volumes that I am reading with SimpleITK import SimpleITK as sitk for filename in filenames: image = sitk.ReadImage(filename) Each of the volumes has different size, spacing, origin and direction. This code yields different values for different images: print(image.GetSize()) print(image.GetOrigin()) print(image.GetSpacing()) print(image.GetDirection()) My question is: how do I transform the images to have the same size and spacing so that they all have the same resolution

SimpleITK Resize images

社会主义新天地 提交于 2021-02-04 14:06:43
问题 I have a set o 3D volumes that I am reading with SimpleITK import SimpleITK as sitk for filename in filenames: image = sitk.ReadImage(filename) Each of the volumes has different size, spacing, origin and direction. This code yields different values for different images: print(image.GetSize()) print(image.GetOrigin()) print(image.GetSpacing()) print(image.GetDirection()) My question is: how do I transform the images to have the same size and spacing so that they all have the same resolution

SimpleITK Resize images

故事扮演 提交于 2021-02-04 14:05:31
问题 I have a set o 3D volumes that I am reading with SimpleITK import SimpleITK as sitk for filename in filenames: image = sitk.ReadImage(filename) Each of the volumes has different size, spacing, origin and direction. This code yields different values for different images: print(image.GetSize()) print(image.GetOrigin()) print(image.GetSpacing()) print(image.GetDirection()) My question is: how do I transform the images to have the same size and spacing so that they all have the same resolution

Bad disparity map using StereoBM in OpenCV

痴心易碎 提交于 2021-02-04 10:49:06
问题 I've put together a stereo cam rig and am having trouble using it to produce a good disparity map. Here's an example of two rectified images and the disparity map I produced with them: As you can see, the results are pretty bad. Changing the StereoBM's settings doesn't change much. The setup Both cameras are the same model and connect to my computer with USB. They are fixed to a rigid wooden board so that they don't move. I aligned them as best I could, but of course it's not perfect. They