3/28/2023 0 Comments Inpaint disparity code![]() Heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR)Ĭv2.imwrite('IMG_1675_Color_Depth_Blurred.png',heatmap) The grayscale depth map image was further processed to create a false color image with the inferno scale using this script. This group of images was then generated into a video with FFMPEG to give a sense of depth and motion. Using GIMP, I generated a series of images by changing the amount of map and displace values. The rgb image is on the left while the grayscale depth map image is on the right. I applied a similar technique with this image. This technique is demonstrated in this video. Some images that lack a secondary viewpoint can have a depth map layer manually created using GIMP. I then manually edited the image in an attempt to reduce holes, pitting, and other noise while adding more contrast to the overall image, here is the result. This created the following grayscale depth map image. Plt.imshow(disparity_SGBM, cmap_reversed) Num_disp = 32 # Needs to be divisible by 16ĭisparity_SGBM = pute(imageLeft, imageRight)Ĭmap_reversed = plt.cm.get_cmap('gray_r') # num_disp = max_disp - min_disp # Needs to be divisible by 16 # specific parameters obtained through trial and error. Note: disparity range is tuned according to ImageRight = cv.imread('resized_v18834-left.png', 0) ImageLeft = cv.imread('resized_v18834-right.png', 0) The following script requires that both images have matching dimensions. First, the stereo card was split into 2 separate images and tonal qualities were applied which gave the best contrast. ![]() I used a different method for processing images into depth with OpenCV from the example image above. The University of Washington contains a digital collection of these artifacts, Stereo cards were a common technique during early photography to give the viewer the illusion of depth. It is available as a free media repository from Wikimedia Commons, This image is of a stereo card from the Missouri History Museum that is dated between 19 which depicts a wasteland as a result from World War 1. Let’s start by leaving flatland.įlatland & the 4th Dimension – Carl Sagan The purpose of this post is to expand on this topic by demonstrating some features and limitations. This topic has been covered in earlier posts in regards to anaglyph imaging and OpenCV disparity mapping. This post will cover the concepts of image processing to generate and display image disparity or depth of field. ![]()
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