Image processing Using openCV
Image Processing Implementations in OPENCV
Step 1: load the image and convert it to a grayscale image After compiling the example code in Section II, in this section, you now have to fill the missing parts in the file “imgprocessing.py”. For this purpose, open “imgprocessing.py” from your IDE or with any text editor. In this step, you will load the image and assign it to a variable (e.g. “img”), then convert it to a grayscale image. When compiling this program, similar to the example code introduced in Section II, you will click on the button “Run file” if you are working on an IDE or, if you are working on the prompt, you will go to the location of your folder
Step 2: apply Histogram Equalization In this step, you will apply histogram equalization to the grayscale image (Output of Step 1). You might use the following functions: cv2.equalizeHist
Step 3: flip the image; first turn the image upside down and then reflect the image in the x direction In this step, you will apply upside down operation and reflection in the x direction to the image for which histogram equalization is applied (Output of Step 2) and show the images computed. You might use the following functions: cv2.flip
Step 4: apply filtering In this step, first, you will apply some filtering (atleast 3) to the image for which histogram equalization is applied (Output of Step 2) and show the image computed. You might use the following functions: cv2.medianBlur, cv2.GaussianBlur, cv2.bilateralFilter (non exhaustive)
Step 5: apply Operator to compute Edge Image In this step, first, you will apply operators to the filtered image (Output of Step 4) and show the resultant edge image (atleast 2 edge detection methods). You might use the following functions: cv2.Laplacian, cv2.Canny, cv2.Sobel (non exhaustive), cv2.convertScaleAbs, cv2.addWeighted