When using the GPU in OpenCV to perform the findContours operation, you first need to import the corresponding module. The GPU module can be imported using the following code:
import cv2
import cv2.cuda
Next, you can use the cv2.cuda.createCannyEdgeDetector() function to create a Canny edge detector GPU object. The parameters of this function can be adjusted as needed. For example:
gpu_canny = cv2.cuda.createCannyEdgeDetector(threshold1=100, threshold2=200)
The image data can then be passed to the GPU using the cv2.cuda.GpuMat() function. For example, assuming the image data is held in the variable img, you can use the following code to pass the image data to the GPU:
gpu_img = cv2.cuda.GpuMat()
gpu_img.upload(img)
Next, you can use the created GPU object to perform edge detection on the image. For example, you can use the following code to perform edge detection on an image:
gpu_edges = gpu_canny.detect(gpu_img)
Finally, you can create a GPU stream object by calling the cv2.cuda.stream.Stream() function, and use the cv2(cuda) function to transfer the processed image data from the GPU back to the CPU. For example:
stream = cv2.cuda.Stream()
gpu_edges.download(edges, stream)
stream.waitForCompletion()
Finally, you can use the cv2.findContours() function to find contours in an image. For example:
contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
Among them, edges is the image data after edge detection, cv2.RETR_EXTERNAL means that only the outermost contour is detected, and cv2.CHAIN_APPROX_SIMPLE means the representation of simplified contours.
The above is a code example of using GPU to perform findContours operation in OpenCV