Created
April 2, 2013 18:52
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OCR with OpenCV - KNN methods from Abid Rahman
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| ''' classify | |
| after training, classify a sample image with K-Nearest Neighbor | |
| annotated from Abid Rahman's post: http://stackoverflow.com/a/9620295/232638 | |
| ''' | |
| import cv2 | |
| import numpy as np | |
| # load the data we generated previously | |
| samples = np.loadtxt('general-samples.data', np.float32) | |
| responses = np.loadtxt('general-responses.data', np.float32) | |
| responses = responses.reshape((responses.size,1)) | |
| # train the KNN model | |
| model = cv2.KNearest() | |
| model.train(samples, responses) | |
| # test the model with another image | |
| image = cv2.imread('pi.png') | |
| out = np.zeros(image.shape, np.uint8) | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # create black and white image | |
| thresh = cv2.adaptiveThreshold(gray, 255, 1, 1, 11, 2) | |
| # find contours | |
| contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST | |
| , cv2.CHAIN_APPROX_SIMPLE) | |
| for contour in contours: | |
| if cv2.contourArea(contour) > 50: | |
| # contour is sufficiently large to possibly be a number | |
| [x, y, w, h] = cv2.boundingRect(contour) | |
| if h > 28: | |
| # sample height is sufficiently large to possibly be a number | |
| # draw the bounding box | |
| cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2) | |
| # select the sample, resize to 10x10 and then vectorize | |
| roi = thresh[y:y+h, x:x+w] | |
| roi_small = cv2.resize(roi,(10,10)) | |
| roi_small = roi_small.reshape((1,100)) | |
| roi_small = np.float32(roi_small) | |
| # find nearest neighbor | |
| retval, results, neigh_resp, dists = model.find_nearest(roi_small | |
| , k = 1) | |
| # extra parens? | |
| string = str(int((results[0][0]))) | |
| # write the result the output image | |
| cv2.putText(out, string, (x, y+h), 0, 1, (0, 255, 0)) | |
| # show the results | |
| cv2.imshow('im',image) | |
| cv2imshow('out',out) | |
| cv2.waitKey(0) |
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| ''' train | |
| open a training image of numbers, train.png | |
| preprocess and find contours | |
| for each contour, prompt user for input as to which number is being displayed | |
| annotated from Abid Rahman's post: http://stackoverflow.com/a/9620295/232638 | |
| ''' | |
| import numpy as np | |
| import cv2 | |
| # open training image for processing | |
| image = cv2.imread('train.png') | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| blur = cv2.GaussianBlur(gray, (5, 5), 0) | |
| # create black and white image | |
| thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2) | |
| # find contorus | |
| contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST | |
| , cv2.CHAIN_APPROX_SIMPLE) | |
| samples = np.empty((0, 100)) | |
| responses = [] | |
| # keyboard mappings for 0-9; user may type in this range when prompted | |
| keys = [i for i in range(48, 58)] | |
| for contour in contours: | |
| if cv2.contourArea(contour) > 50: | |
| # sufficiently large contour to possibly be a number | |
| [x, y, w, h] = cv2.boundingRect(contour) | |
| if h > 28: | |
| # tall enough to possibly be a number | |
| # draw the bounding box on the image | |
| cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2) | |
| roi = thresh[y:y+h, x:x+w] | |
| roi_small = cv2.resize(roi, (10, 10)) | |
| # show image and wait for keypress | |
| cv2.imshow('norm', image) | |
| key = cv2.waitKey(0) | |
| if key == 27: | |
| sys.exit() | |
| elif key in keys: | |
| # save pixel data in 1x100 matrix of 'samples' | |
| sample = roi_small.reshape((1,100)) | |
| samples = np.append(samples,sample,0) | |
| # save input in 'responses' | |
| responses.append(int(chr(key))) | |
| print "training complete" | |
| np.savetxt('general-samples.data', samples) | |
| responses = np.array(responses, np.float32) | |
| responses = responses.reshape((responses.size,1)) | |
| np.savetxt('general-responses.data', responses) |
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dude, how to class shapes in image and not laters using this code?