img is cropped and transformed to torch
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shaaqu
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@ -19,16 +19,10 @@
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@ -88,7 +82,7 @@
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@ -52,6 +52,7 @@ plt.show()
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with torch.no_grad():
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logps = model(img)
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print(logps)
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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@ -17,6 +17,7 @@ train_set = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True,
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val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
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train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
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val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
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print(train_set[0])
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# building nn model
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input_size = 784 # = 28*28
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@ -4,10 +4,19 @@ import imutils
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import cv2
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import matplotlib.pyplot as plt
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import torch
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from matplotlib import cm
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from torch import nn
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from PIL import Image
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from skimage.feature import hog
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from torchvision.transforms import transforms
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code = []
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path = "test1.jpg"
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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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img = cv2.imread(path)
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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@ -19,18 +28,44 @@ ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_
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rects = [cv2.boundingRect(ctr) for ctr in ctrs]
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# load nn model
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input_size = 784 # = 28*28
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hidden_sizes = [128, 128, 64]
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output_size = 10
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model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[2], output_size),
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nn.LogSoftmax(dim=-1))
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model.load_state_dict(torch.load('digit_reco_model2.pt'))
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model.eval()
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for rect in rects:
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# Draw the rectangles
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cv2.rectangle(img, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
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# Make the rectangular region around the digit
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leng = int(rect[3] * 1.6)
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pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
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pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
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roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
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# Crop image
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crop_img = img[rect[1]:rect[1] + rect[3], rect[0]:rect[0] + rect[2]]
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plt.imshow(crop_img)
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plt.show()
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# Resize the image
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roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
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roi = cv2.dilate(roi, (3, 3))
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# Calculate the HOG features
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roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_AREA)
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plt.imshow(roi)
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plt.show()
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im = Image.fromarray(roi)
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im = transform(im)
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print(im)
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plt.imshow(im)
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plt.show()
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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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print(ps[0])
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probab = list(ps.numpy()[0])
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print("Predicted Digit =", probab.index(max(probab)))
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cv2.imshow("Resulting Image with Rectangular ROIs", img)
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cv2.waitKey()
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