Normalize line endings and whitespace
This commit is contained in:
OpenCV Buildbot
committed by
Andrey Kamaev
Andrey Kamaev
parent
69020da607
commit
04384a71e4
168
samples/python2/digits_video.py
Normal file → Executable file
168
samples/python2/digits_video.py
Normal file → Executable file
@@ -1,84 +1,84 @@
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import numpy as np
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import cv2
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import os
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import sys
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import video
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from common import mosaic
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from digits import *
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def main():
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try: src = sys.argv[1]
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except: src = 0
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cap = video.create_capture(src)
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classifier_fn = 'digits_svm.dat'
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if not os.path.exists(classifier_fn):
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print '"%s" not found, run digits.py first' % classifier_fn
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return
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model = SVM()
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model.load(classifier_fn)
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while True:
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ret, frame = cap.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
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bin = cv2.medianBlur(bin, 3)
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contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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try: heirs = heirs[0]
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except: heirs = []
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for cnt, heir in zip(contours, heirs):
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_, _, _, outer_i = heir
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if outer_i >= 0:
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continue
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x, y, w, h = cv2.boundingRect(cnt)
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if not (16 <= h <= 64 and w <= 1.2*h):
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continue
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pad = max(h-w, 0)
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x, w = x-pad/2, w+pad
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cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
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bin_roi = bin[y:,x:][:h,:w]
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gray_roi = gray[y:,x:][:h,:w]
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m = bin_roi != 0
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if not 0.1 < m.mean() < 0.4:
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continue
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'''
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v_in, v_out = gray_roi[m], gray_roi[~m]
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if v_out.std() > 10.0:
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continue
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s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
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cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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'''
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s = 1.5*float(h)/SZ
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m = cv2.moments(bin_roi)
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c1 = np.float32([m['m10'], m['m01']]) / m['m00']
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c0 = np.float32([SZ/2, SZ/2])
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t = c1 - s*c0
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A = np.zeros((2, 3), np.float32)
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A[:,:2] = np.eye(2)*s
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A[:,2] = t
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bin_norm = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
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bin_norm = deskew(bin_norm)
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if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
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frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
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sample = preprocess_hog([bin_norm])
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digit = model.predict(sample)[0]
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cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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cv2.imshow('frame', frame)
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cv2.imshow('bin', bin)
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ch = cv2.waitKey(1)
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if ch == 27:
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break
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if __name__ == '__main__':
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main()
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import numpy as np
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import cv2
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import os
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import sys
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import video
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from common import mosaic
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from digits import *
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def main():
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try: src = sys.argv[1]
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except: src = 0
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cap = video.create_capture(src)
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classifier_fn = 'digits_svm.dat'
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if not os.path.exists(classifier_fn):
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print '"%s" not found, run digits.py first' % classifier_fn
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return
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model = SVM()
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model.load(classifier_fn)
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while True:
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ret, frame = cap.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
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bin = cv2.medianBlur(bin, 3)
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contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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try: heirs = heirs[0]
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except: heirs = []
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for cnt, heir in zip(contours, heirs):
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_, _, _, outer_i = heir
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if outer_i >= 0:
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continue
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x, y, w, h = cv2.boundingRect(cnt)
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if not (16 <= h <= 64 and w <= 1.2*h):
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continue
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pad = max(h-w, 0)
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x, w = x-pad/2, w+pad
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cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
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bin_roi = bin[y:,x:][:h,:w]
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gray_roi = gray[y:,x:][:h,:w]
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m = bin_roi != 0
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if not 0.1 < m.mean() < 0.4:
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continue
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'''
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v_in, v_out = gray_roi[m], gray_roi[~m]
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if v_out.std() > 10.0:
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continue
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s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
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cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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'''
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s = 1.5*float(h)/SZ
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m = cv2.moments(bin_roi)
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c1 = np.float32([m['m10'], m['m01']]) / m['m00']
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c0 = np.float32([SZ/2, SZ/2])
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t = c1 - s*c0
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A = np.zeros((2, 3), np.float32)
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A[:,:2] = np.eye(2)*s
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A[:,2] = t
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bin_norm = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
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bin_norm = deskew(bin_norm)
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if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
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frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
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sample = preprocess_hog([bin_norm])
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digit = model.predict(sample)[0]
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cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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cv2.imshow('frame', frame)
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cv2.imshow('bin', bin)
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ch = cv2.waitKey(1)
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if ch == 27:
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break
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if __name__ == '__main__':
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main()
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