forked from s444420/AL-2020
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
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import cv2
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import torch
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from nn_model import Net
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from torchvision.transforms import transforms
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def recognizer(path):
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codes = []
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code = []
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,)),
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])
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# load nn model
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model = Net()
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model.load_state_dict(torch.load('model.pt'))
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model.eval()
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img = cv2.imread(path)
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)
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ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV)
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ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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rects = [cv2.boundingRect(ctr) for ctr in ctrs]
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for rect in rects:
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# Crop image
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crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
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# Resize the image
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roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC)
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# roi = cv2.dilate(roi, (3, 3))
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# plt.imshow(roi)
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# plt.show()
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im = transform(roi)
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im = im.view(1, 1, 28, 28)
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with torch.no_grad():
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logps = model(im)
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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code.append(probab.index(max(probab)))
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codes.append(code)
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# cv2.imshow("Code", img)
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# cv2.waitKey()
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return codes
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