detecting digits
This commit is contained in:
shaaqu
parent
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commit
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@ -19,12 +19,17 @@
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After Width: | Height: | Size: 10 KiB |
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@ -4,47 +4,49 @@ import torchvision
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import matplotlib.pyplot as plt
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from time import time
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from torchvision import datasets, transforms
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from torch import nn, optim, nn, optim
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from torch import nn, optim
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import cv2
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def view_classify(img, ps):
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''' Function for viewing an image and it's predicted classes.
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'''
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ps = ps.data.numpy().squeeze()
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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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fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
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ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())
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ax1.axis('off')
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ax2.barh(np.arange(10), ps)
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ax2.set_aspect(0.1)
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ax2.set_yticks(np.arange(10))
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ax2.set_yticklabels(np.arange(10))
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ax2.set_title('Class Probability')
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ax2.set_xlim(0, 1.1)
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plt.tight_layout()
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# load nn model
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model = torch.load('digit_reco_model2.pt')
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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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# model = torch.load('digit_reco_model2.pt')
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if model is None:
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print("Model is not loaded.")
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else:
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print("Model is loaded.")
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# image
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img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
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img = cv2.blur(img, (9, 9)) # poprawia jakosc
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img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
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img = img.reshape((len(img), -1))
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print(type(img))
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# print(img.shape)
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# plt.imshow(img ,cmap='binary')
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# plt.show()
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img = np.array(img, dtype=np.float32)
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img = torch.from_numpy(img)
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img = img.view(1, 784)
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# img from dataset
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val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
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val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
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images, labels = next(iter(val_loader))
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print(type(images))
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img = images[0].view(1, 784)
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plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
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plt.show()
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# recognizing
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@ -54,5 +56,3 @@ with torch.no_grad():
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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print("Predicted Digit =", probab.index(max(probab)))
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view_classify(img.view(1, 28, 28), ps)
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Binary file not shown.
Binary file not shown.
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@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
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from time import time
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from torchvision import datasets, transforms
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from torch import nn, optim
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import cv2
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# IMG transform
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transform = transforms.Compose([transforms.ToTensor(),
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@ -17,15 +18,6 @@ val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, tr
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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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data_iter = iter(train_loader)
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images, labels = data_iter.next()
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print(images.shape)
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print(labels.shape)
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plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
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plt.show()
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# building nn model
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input_size = 784 # = 28*28
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hidden_sizes = [128, 128, 64]
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@ -48,15 +40,12 @@ images = images.view(images.shape[0], -1)
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logps = model(images) # log probabilities
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loss = criterion(logps, labels) # calculate the NLL loss
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# print('Before backward pass: \n', model[0].weight.grad)
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loss.backward()
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# print('After backward pass: \n', model[0].weight.grad)
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# training
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optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
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time0 = time()
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epochs = 100
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epochs = 1
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for e in range(epochs):
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running_loss = 0
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for images, labels in train_loader:
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# testing
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images, labels = next(iter(val_loader))
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print(type(images))
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img = images[0].view(1, 784)
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with torch.no_grad():
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logps = model(img)
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@ -115,5 +103,5 @@ print("\nModel Accuracy =", (correct_count / all_count))
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# saving model
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# torch.save(model, './digit_reco_model.pt')
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torch.save(model, './digit_reco_model2.pt')
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# torch.save(model.state_dict(), './digit_reco_model.pt')
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# torch.save(model.state_dict(), './digit_reco_model2.pt')
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@ -6,6 +6,7 @@ from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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import pandas as pd
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import cv2
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import keras
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# 28x28
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train_data = np.genfromtxt('dataset/train.csv', delimiter=',', skip_header=1, max_rows=20000, encoding='utf-8')
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After Width: | Height: | Size: 14 KiB |
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@ -3,35 +3,34 @@ import argparse
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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 PIL import Image
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img = cv2.cvtColor(cv2.imread('barcode.jpg'), cv2.COLOR_BGR2GRAY)
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path = "test1.jpg"
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ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
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X = cv2.Sobel(img, ddepth=ddepth, dx=1, dy=0, ksize=-1)
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Y = cv2.Sobel(img, ddepth=ddepth, dx=0, dy=1, ksize=-1)
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img = cv2.imread(path)
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gradient = cv2.subtract(X, Y)
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gradient = cv2.convertScaleAbs(gradient)
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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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blurred = cv2.blur(gradient, (9, 9))
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(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
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ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV)
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
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closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
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closed = cv2.erode(closed, None, iterations=4)
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closed = cv2.dilate(closed, None, iterations=4)
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ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
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rects = [cv2.boundingRect(ctr) for ctr in ctrs]
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rect = cv2.minAreaRect(c)
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box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
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box = np.int0(box)
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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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# Resize the image
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roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
|
||||
roi = cv2.dilate(roi, (3, 3))
|
||||
# Calculate the HOG features
|
||||
|
||||
cv2.drawContours(img, [box], -1, (0, 255, 0), 3)
|
||||
cv2.imshow("Image", img)
|
||||
cv2.waitKey(0)
|
||||
|
||||
plt.imshow(closed ,cmap='binary')
|
||||
plt.show()
|
||||
cv2.imshow("Resulting Image with Rectangular ROIs", img)
|
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cv2.waitKey()
|
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Reference in New Issue