import cv2 import torch from nn_model import Net from torchvision.transforms import transforms def recognizer(paths): codes = [] code = [] transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ]) # load nn model model = Net() model.load_state_dict(torch.load('model.pt')) model.eval() for path in paths: img = cv2.imread(path) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0) ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV) ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) rects = [cv2.boundingRect(ctr) for ctr in ctrs] for rect in rects: # Crop image crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0] # Resize the image roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC) # roi = cv2.dilate(roi, (3, 3)) # plt.imshow(roi) # plt.show() im = transform(roi) im = im.view(1, 1, 28, 28) with torch.no_grad(): logps = model(im) ps = torch.exp(logps) probab = list(ps.numpy()[0]) code.append(probab.index(max(probab))) codes.append(code) # cv2.imshow("Code", img) # cv2.waitKey() return codes