我们从Python开源项目中，提取了以下22个代码示例，用于说明如何使用cv2.compareHist()。
def match_hists(hists): matches = {} idx = 0 for i in range(len(hists)): for j in range(len(hists[i])): matches['{}_{}'.format(i,j)] = idx idx += 1 for i in range(len(hists)): for j in range(i+1, len(hists)): for k in range(len(hists[i])): corrs = [] for l in range(len(hists[j])): corr = cv2.compareHist(hists[i][k], hists[j][l], cv2.cv.CV_COMP_INTERSECT) corrs.append(corr) corrs = np.array(corrs) idxs = np.argsort(corrs) if corrs[idxs[-1]] > 0.80: matches['{}_{}'.format(j, idxs[-1])] = matches['{}_{}'.format(i, k)] return matches
def predict(img): '''??????????''' if len(_histograms)==0: print 'model is not build' return img=cv2.resize(img,(48,48)) lbP_img=recognition.CircularLBP(img,radius=_radius,neighbors=_neighbors) lbph_pre=recognition.LBPH(lbP_img,int(math.pow(2,_neighbors)),grid_x=_grid_x,grid_y=_grid_y) minDist=sys.float_info.max minClass=-1 for index in range(len(_histograms)): dist=cv2.compareHist(_histograms[index],lbph_pre,cv2.HISTCMP_CHISQR) if dist<minDist: minDist=dist minClass=_labels[index] print 'label:%d distance:%f'%(minClass,minDist) return minClass,minDist,lbP_img,lbph_pre
def main(): target_im = cv2.imread(sys.argv[1]) target_hist = cv2.calcHist([target_im], [0], None, [256], [0, 256]) for i in list: comparing_im = cv2.imread(i) comparing_hist = cv2.calcHist([comparing_im], [0], None, [256], [0, 256]) diff = cv2.compareHist(target_hist, comparing_hist, 0) if diff > float(sys.argv[2]): print i, print diff
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_CORREL)
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_CHISQR)
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_INTERSECT)
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_HELLINGER)
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_BHATTACHARYYA)
def compare(self, h1, h2): return cv2.compareHist(h1, h2, cv2.HISTCMP_KL_DIV)
def are_similar(self, first, second): result = 0 for i in xrange(3): hist1 = cv2.calcHist([first], [i], None, [256], [0,256]) hist2 = cv2.calcHist([second], [i], None, [256], [0,256]) result += cv2.compareHist(hist1, hist2, self.get_technique()) return result / 3
def matches(self,frame,rects): cal=lambda x:Match.calHist(frame,x) hists=list(map(cal,rects)) print(list(map(lambda x:cv2.compareHist(x,self.hist,cv2.HISTCMP_BHATTACHARYYA),hists)))
def gray_histogram_cmp_corr(self): self.threshold = .9 gray_i = cv2.cvtColor(self.image_i, cv2.COLOR_BGR2GRAY) hist_i = cv2.calcHist([gray_i], [0], None, [256], [0, 256]) gray_j = cv2.cvtColor(self.image_j, cv2.COLOR_BGR2GRAY) hist_j = cv2.calcHist([gray_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold)
def gray_histogram_cmp_bhatta(self): self.threshold = .07 gray_i = cv2.cvtColor(self.image_i, cv2.COLOR_BGR2GRAY) hist_i = cv2.calcHist([gray_i], [0], None, [256], [1, 256]) gray_j = cv2.cvtColor(self.image_j, cv2.COLOR_BGR2GRAY) hist_j = cv2.calcHist([gray_j], [0], None, [256], [1, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_BHATTACHARYYA) self.assertGreater(self.measure, self.threshold)
def gray_histogram_cmp_bhatta(self): threshold = .07 gray_i = cv2.cvtColor(self.image_i, cv2.COLOR_BGR2GRAY) hist_i = cv2.calcHist([gray_i], [0], None, [256], [1, 256]) gray_j = cv2.cvtColor(self.image_j, cv2.COLOR_BGR2GRAY) hist_j = cv2.calcHist([gray_j], [0], None, [256], [1, 256]) measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_BHATTACHARYYA) self.assertGreater(measure, threshold)
def Harris_Corner(self): self.threshold = 0.999999999999 temp_i = self.image_i.copy() temp1_i = self.image_i.copy() gray_i = cv2.cvtColor(temp_i, cv2.COLOR_BGR2GRAY) gray_i = numpy.float32(gray_i) dst_i = cv2.cornerHarris(gray_i,2,3,0.025) dst_i = cv2.dilate(dst_i,None) # Threshold for an optimal value, it may vary depending on the image. temp_i[dst_i<0.01*dst_i.max()]=[0,0,0] temp_i[dst_i>=0.01*dst_i.max()]=[255,255,255] temp1_i[dst_i>0.01*dst_i.max()]=[0,0,255] hist_i = cv2.calcHist([temp_i], [0], None, [256], [0, 256]) temp_j = self.image_j.copy() temp1_j = self.image_j.copy() gray_j = cv2.cvtColor(temp_j, cv2.COLOR_BGR2GRAY) gray_j = numpy.float32(gray_j) dst_j = cv2.cornerHarris(gray_j,2,3,0.025) dst_j = cv2.dilate(dst_j,None) # Threshold for an optimal value, it may vary depending on the image. temp_j[dst_j<0.01*dst_j.max()]=[0,0,0] temp_j[dst_j>=0.01*dst_j.max()]=[255,255,255] temp1_j[dst_j>0.01*dst_j.max()]=[0,0,255] hist_j = cv2.calcHist([temp_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold) print self.measure cv2.imshow('Input X',temp1_i) cv2.waitKey(0) cv2.imshow('Input Y',temp1_j) cv2.waitKey(0)
def Canny_edge(self): self.threshold = .999999999999999 gray_i = cv2.cvtColor(self.image_i, cv2.COLOR_BGR2GRAY) edges_i = cv2.Canny(gray_i,100,200) gray_j = cv2.cvtColor(self.image_j, cv2.COLOR_BGR2GRAY) edges_j = cv2.Canny(gray_j,100,200) hist_i = cv2.calcHist([edges_i], [0], None, [256], [0, 256]) hist_j = cv2.calcHist([edges_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold) print self.measure # cv2.imshow(self.image_i) # cv2.imshow(self.image_j)
def Harris_Corner(self): self.threshold = 0.999999999999 temp_i = self.image_i.copy() temp1_i = self.image_i.copy() gray_i = cv2.cvtColor(temp_i, cv2.COLOR_BGR2GRAY) gray_i = numpy.float32(gray_i) dst_i = cv2.cornerHarris(gray_i,2,3,0.025) dst_i = cv2.dilate(dst_i,None) # Threshold for an optimal value, it may vary depending on the image. temp_i[dst_i<0.01*dst_i.max()]=[0,0,0] temp1_i[dst_i>0.01*dst_i.max()]=[0,0,255] hist_i = cv2.calcHist([temp_i], [0], None, [256], [0, 256]) temp_j = self.image_j.copy() temp1_j = self.image_j.copy() gray_j = cv2.cvtColor(temp_j, cv2.COLOR_BGR2GRAY) gray_j = numpy.float32(gray_j) dst_j = cv2.cornerHarris(gray_j,2,3,0.025) dst_j = cv2.dilate(dst_j,None) # Threshold for an optimal value, it may vary depending on the image. temp_j[dst_j<0.01*dst_j.max()]=[0,0,0] temp1_j[dst_j>0.01*dst_j.max()]=[0,0,255] hist_j = cv2.calcHist([temp_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold)
def rgb_histogram(self): self.threshold = 0.999999999999999 hist_i = cv2.calcHist([self.image_i], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) hist_j = cv2.calcHist([self.image_j], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) hist_flatten_i = hist_i.flatten() hist_flatten_j = hist_j.flatten() self.measure = cv2.compareHist(hist_flatten_i, hist_flatten_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold)
def __get_uniq_faces_curr_frame(self, frame_id, faces_roi_hists_prev, faces_roi_hists): faces_prev = len(faces_roi_hists_prev) faces_curr = len(faces_roi_hists) logger.info("[{0}] Face Similarity: Prev: {1}, Curr: {2}".format(frame_id, faces_prev, faces_curr)) # First Time if faces_prev == 0: return faces_curr # Current frame has more faces than prev frame #if faces_curr > faces_prev: # return faces_curr - faces_prev uniq_faces_curr_frame = 0 # Perform Image Histogram Similarity # For each histogram in current frame for rh1 in faces_roi_hists: match_found = False # For each histogram in previous frame for rh2 in faces_roi_hists_prev: #print "\nrh1 {0}: {1}".format(type(rh1),np.shape(rh1)) #print "\nrh2 {0}: {1}".format(type(rh2),np.shape(rh2)) corr = cv2.compareHist(rh1, rh2, cv2.HISTCMP_CORREL) logger.info("[{0}] Similarity Metrics: {1}".format(frame_id, corr)) if corr >= 0.35: # Match Found, can break the loop for this histogram in current frame match_found = True break; # Add to unique face count, if no match found for this histogram in current frame if not match_found: uniq_faces_curr_frame += 1 logger.info("[{0}] Total Unique Faces in Current Frame: {1}".format(frame_id, uniq_faces_curr_frame)) return uniq_faces_curr_frame
