![]() The images are constructed using mathematical formulas describing colors, shapes, and placement. If you want, you can try tweaking around with the threshold value in the program to see if it'll get you a more divided image.A vector, on the other hand, is also known as object-oriented graphics. There may be other filters you can use to get more accurate results, but I think identifying a threshold is a baseline image segmentation technique. Instance segmentation and dividing images has actually been a very big computer science problem for the past decade or so. If you run the code, you can see that the output is extremely inaccurate and that the cob and the kernels of corn are barely visible. New_img = np.array(threshold_applied, np.uint8)Ĭv2.imwrite(str(threshold)+".jpg", new_img) To test different threshold values, simply check if each pixel value is above or below a certain value (that you pick): import numpy as npĭef apply_threshold(img_array, threshold): First, convert the whole image to grayscale w. To make a rough outline of the image, you'll have to experiment with different threshold values. Here is an article on how you can do that: Ī more simple (but way less accurate) solution might be to just find use RGB threshold values to create an "outline" of the picture and then use a flood fill algorithm to discern specific pixels in the resulting image. This process is best done with deep learning techniques which might not suit you unless you can find a pre-trained model. I believe what you are trying to do is something called "Instance Segmentation". #cv2.imwrite(path_to_save_segments+tmp_image_name, result)Įxcuse the commented out code that's me just observing the changes I make to the image as I modify the algorithm. #tmp_image_name= image_name + "-kmeans-" + str(K) + str(random.random()) + ".png" Resized = cv2.resize(result, dim, interpolation = cv2.INTER_AREA) Height = int(edges.shape * scale_percent / 100) Width = int(edges.shape * scale_percent / 100) Scale_percent = 30 # percent of original size Result = cv2.bitwise_and(img, img, mask=mask) #cv2.imwrite(path_to_save_segments+tmp_image_name, cropped_contour) #tmp_image_name= image_name + "-kmeans-" + str(K) + str(random.random()) + ".jpg" Print("Saving segments", len(sorted_contours))įor (i,c) in tqdm(enumerate(sorted_contours)): Mask = np.zeros(img.shape, dtype=img.dtype)Īrray_of_contour_areas = Ĭontour_avg = sum(array_of_contour_areas)/len(array_of_contour_areas)Ĭontour_var = sum(pow(x-contour_avg,2) for x in array_of_contour_areas) / len(array_of_contour_areas) Sorted_contours= sorted(contours, key=cv2.contourArea, reverse= True) ![]() ![]() Ret,label,center=cv2.kmeans(img_reshaped,K,None,criteria,attempts,cv2.KMEANS_PP_CENTERS)Įdges = cv2.Canny(img_gray, lower, upper)Ĭontours, hierarchy = cv2.findContours(py(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) Img_gray = cv2.cvtColor(img_blur, cv2.COLOR_BGR2GRAY)Ĭriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) Img = cv2.imread(path_to_images+image_name) What I have tried is the following: def kmeansSegmentation(path_to_images, image_name, path_to_save_segments): What I would like to do is (using OpenCV) separate the corncob from the green stock it's attached to, and separate each corncob chunk into its own images. ![]() Here is an example picture I'm trying to "vectorize": I don't know what it's actually called, but what I'm trying to do is take the elements of an image and separate them into different images. I'm using the term "vectorize" because that is what has been used to describe the process I'm writing about.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |