83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
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from skimage import feature
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import matplotlib.pyplot as plt
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import numpy as np
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import cv2
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method_distance=[cv2.HISTCMP_CORREL,
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cv2.HISTCMP_CHISQR,
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cv2.HISTCMP_INTERSECT,
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cv2.HISTCMP_BHATTACHARYYA,
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cv2.HISTCMP_HELLINGER,
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cv2.HISTCMP_CHISQR_ALT,
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cv2.HISTCMP_KL_DIV]
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method_lbp=['default',
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'ror',
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'uniform',
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'var']
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cap=cv2.VideoCapture(0)
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numPoints=24
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radius=3
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image_ref=None
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width=320
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height=240
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windowsize_r=30
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windowsize_c=30
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rows=int(height/windowsize_r)
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cols=int(width/windowsize_c)
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tab_score=np.empty((rows, cols), dtype=np.float32)
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id_method_distance=6
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id_method_lbp=2
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seuil=0.3
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while True:
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ret, frame=cap.read()
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image=cv2.resize(frame, (width, height))
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if image_ref is not None:
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gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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for r in range(rows):
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for c in range(cols):
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window=gray[r*windowsize_r:(r+1)*windowsize_r, c*windowsize_c:(c+1)*windowsize_c]
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lbp=feature.local_binary_pattern(window, numPoints, radius, method=method_lbp[id_method_lbp])
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hist, _=np.histogram(lbp, normed=True, bins=numPoints, range=(0, numPoints))
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score=cv2.compareHist(hist.astype(np.float32), hist_ref.astype(np.float32), method_distance[id_method_distance])
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tab_score[r, c]=score
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if image_ref is not None:
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tab_score2=np.zeros_like(tab_score)
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tab_score2[tab_score<seuil]=1
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cv2.imshow("SCORE2", cv2.resize(tab_score2, (int(width), int(height)), interpolation = cv2.INTER_NEAREST))
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tab_score=np.clip(1/(10*tab_score), 0., 1.)
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cv2.imshow("Score", cv2.resize(tab_score, (int(width), int(height)), interpolation = cv2.INTER_NEAREST))
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cv2.rectangle(frame, (0, 0), (frame.shape[1], 30), (100, 100, 100), cv2.FILLED)
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txt="[q]Quit [p|m]Seuil={:2.1f} [d]Methode distance:{} [l]Methode LBP:{}".format(seuil, method_distance[id_method_distance], method_lbp[id_method_lbp])
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cv2.putText(frame, txt, (20, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
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cv2.imshow("Image", frame)
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key=cv2.waitKey(1)&0xFF
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if key==ord('q'):
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quit()
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if key==ord('i'):
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image_ref=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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lbp_ref=feature.local_binary_pattern(image_ref, numPoints, radius, method=method_lbp[id_method_lbp])
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hist_ref, _=np.histogram(lbp_ref, normed=True, bins=numPoints, range=(0, numPoints))
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if key==ord('l'):
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id_method_lbp=(id_method_lbp+1)%len(method_lbp)
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if image_ref is not None:
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lbp_ref=feature.local_binary_pattern(image_ref, numPoints, radius, method=method_lbp[id_method_lbp])
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hist_ref, _=np.histogram(lbp_ref, normed=True, bins=numPoints, range=(0, numPoints))
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if key==ord('d'):
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id_method_distance=(id_method_distance+1)%len(method_distance)
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if key==ord('p'):
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seuil+=0.1
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if key==ord('m'):
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seuil=max(0.1, seuil-0.1)
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