我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用cv2.connectedComponentsWithStats()。
def getmarkerboundingrect(img, mkpos, mksize): buffer = int(mksize * 0.15) x = mkpos[0] - buffer y = mkpos[1] - buffer w = mksize + buffer*2 h = mksize + buffer*2 roi = img[y:y+h, x:x+w] grayroi = getgrayimage(roi) ret, binimage = cv2.threshold(grayroi,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(binimage) # stats[0], centroids[0] are for the background label. ignore # cv2.CC_STAT_LEFT, cv2.CC_STAT_TOP, cv2.CC_STAT_WIDTH, cv2.CC_STAT_HEIGHT lblareas = stats[1:,cv2.CC_STAT_AREA] imax = max(enumerate(lblareas), key=(lambda x: x[1]))[0] + 1 boundingrect = Rect(stats[imax, cv2.CC_STAT_LEFT], stats[imax, cv2.CC_STAT_TOP], stats[imax, cv2.CC_STAT_WIDTH], stats[imax, cv2.CC_STAT_HEIGHT]) return boundingrect.addoffset((x,y))
def getmarkercenter(image, pos): mkradius = getapproxmarkerradius(image) buffer = int(mkradius * 0.15) roisize = mkradius + buffer # half of the height or width x = pos[0] - roisize y = pos[1] - roisize w = 2 * roisize h = 2 * roisize roi = image[y:y+h, x:x+w] grayroi = getgrayimage(roi) ret, binimage = cv2.threshold(grayroi,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(binimage) # stats[0], centroids[0] are for the background label. ignore lblareas = stats[1:,cv2.CC_STAT_AREA] ave = np.average(centroids[1:], axis=0, weights=lblareas) return tuple(np.array([x, y]) + ave) # weighted average pos of centroids
def MoG2(vid, min_thresh=800, max_thresh=10000): ''' Args : Video object and threshold parameters Returns : None ''' cap = cv2.VideoCapture(vid) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) fgbg = cv2.createBackgroundSubtractorMOG2() connectivity = 4 while(cap.isOpened()): ret, frame = cap.read() if not ret: break fgmask = fgbg.apply(frame) fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) output = cv2.connectedComponentsWithStats( fgmask, connectivity, cv2.CV_32S) for i in range(output[0]): if output[2][i][4] >= min_thresh and output[2][i][4] <= max_thresh: cv2.rectangle(frame, (output[2][i][0], output[2][i][1]), ( output[2][i][0] + output[2][i][2], output[2][i][1] + output[2][i][3]), (0, 255, 0), 2) cv2.imshow('detection', frame) cap.release() cv2.destroyAllWindows()
def select_largest_obj(self, img_bin, lab_val=255, fill_holes=False, smooth_boundary=False, kernel_size=15): '''Select the largest object from a binary image and optionally fill holes inside it and smooth its boundary. Args: img_bin (2D array): 2D numpy array of binary image. lab_val ([int]): integer value used for the label of the largest object. Default is 255. fill_holes ([boolean]): whether fill the holes inside the largest object or not. Default is false. smooth_boundary ([boolean]): whether smooth the boundary of the largest object using morphological opening or not. Default is false. kernel_size ([int]): the size of the kernel used for morphological operation. Default is 15. Returns: a binary image as a mask for the largest object. ''' n_labels, img_labeled, lab_stats, _ = \ cv2.connectedComponentsWithStats(img_bin, connectivity=8, ltype=cv2.CV_32S) largest_obj_lab = np.argmax(lab_stats[1:, 4]) + 1 largest_mask = np.zeros(img_bin.shape, dtype=np.uint8) largest_mask[img_labeled == largest_obj_lab] = lab_val # import pdb; pdb.set_trace() if fill_holes: bkg_locs = np.where(img_labeled == 0) bkg_seed = (bkg_locs[0][0], bkg_locs[1][0]) img_floodfill = largest_mask.copy() h_, w_ = largest_mask.shape mask_ = np.zeros((h_ + 2, w_ + 2), dtype=np.uint8) cv2.floodFill(img_floodfill, mask_, seedPoint=bkg_seed, newVal=lab_val) holes_mask = cv2.bitwise_not(img_floodfill) # mask of the holes. largest_mask = largest_mask + holes_mask if smooth_boundary: kernel_ = np.ones((kernel_size, kernel_size), dtype=np.uint8) largest_mask = cv2.morphologyEx(largest_mask, cv2.MORPH_OPEN, kernel_) return largest_mask
def binary_morph(img, thresh=50, min_size=None, mask_only=True): if min_size is None: # default to 10% of largest image dimension min_size = float(max(img.shape)) * .1 if len(img.shape) == 3: # flatten if RGB image img = np.mean(img, 2).astype(np.uint8) # Apply binary threshold and erode ret, thresh_im = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY) # Connected component labelling n, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh_im) mask = np.zeros_like(labels) # Loop through areas in order of size areas = [s[4] for s in stats] sorted_idx = np.argsort(areas) for lidx, cc in zip(sorted_idx, [areas[s] for s in sorted_idx][:-1]): if cc > min_size: mask[labels == lidx] = 1 if mask_only: return mask * 255 return np.dstack([img * mask] * 3).astype(np.uint8)
def extract_tracked_object(self, image, prev_cc_center): contour_found = False dist = 0.0 tracked_px_count = 0 tracked_object_stats = None largest_centroid = None mask = self.extract_foreground_mask(image) bin_mask = mask.copy() bin_mask[bin_mask < MaskLabel.PERSISTENCE_LABEL.value] = 0 bin_mask[bin_mask > 0] = 1 labels, stats, centroids = cv2.connectedComponentsWithStats(bin_mask, ltype=cv2.CV_16U)[1:4] if len(stats) > 1: # initially, just grab the biggest connected component ix_of_tracked_component = np.argmax(stats[1:, 4]) + 1 largest_centroid = centroids[ix_of_tracked_component].copy() tracking_ok = True if prev_cc_center is not None: a = prev_cc_center b = largest_centroid dist = np.linalg.norm(a - b) # check to make sure we're not too far from the previously-detected blob if dist > 50: dists = np.linalg.norm(centroids - a, axis=1) ix_of_tracked_component = np.argmin(dists) if dists[ix_of_tracked_component] > ConnectedComponentThreshold.TRACK_DIST_THRESH.value: tracking_ok = False largest_centroid = centroids[ix_of_tracked_component].copy() tracked_px_count = stats[ix_of_tracked_component, 4] tracked_object_stats = stats[ix_of_tracked_component] contour_found = tracked_px_count > ConnectedComponentThreshold.HIDDEN.value and tracking_ok if contour_found: bin_mask[labels != ix_of_tracked_component] = 0 mask[bin_mask == 0] = 0 return mask, bin_mask, contour_found, dist, tracked_px_count, tracked_object_stats, largest_centroid
def splitimage(image): dpmm = min(image.shape[0:2]) / DOCSIZE[0] sizethresh = SIZE_THRESH_MM * dpmm uprightimg = makeupright(image) grayimg = getgrayimage(uprightimg) # top line top = grayimg[0,:] sepx = [0,] ret, binimg = cv2.threshold(top,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(binimg) for i in range(1,nlabels): if stats[i,cv2.CC_STAT_AREA] >= sizethresh: sepx.append(centroids[i][1]) # left line left = grayimg[:,0] sepy = [0,] ret, binimg = cv2.threshold(left,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(binimg) for i in range(1,nlabels): if stats[i,cv2.CC_STAT_AREA] >= sizethresh: sepy.append(centroids[i][1]) # divide into images imgs = [] for iy in range(len(sepy)): for ix in range(len(sepx)): if iy == len(sepy) - 1: if ix == len(sepx) - 1: #right-bottom corner imgs.append(uprightimg[int(sepy[iy]):,int(sepx[ix]):]) else: #bottom end imgs.append(uprightimg[int(sepy[iy]):,int(sepx[ix]):int(sepx[ix+1])]) else: if ix == len(sepx) - 1: #right end imgs.append(uprightimg[int(sepy[iy]):int(sepy[iy+1]),int(sepx[ix]):]) else: #others imgs.append(uprightimg[int(sepy[iy]):int(sepy[iy+1]),int(sepx[ix]):int(sepx[ix+1])]) return imgs
