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.Laplacian()。
def try_approximate_corners_blur(self, board_dims, sharpness_threshold): sharpness = cv2.Laplacian(self.frame, cv2.CV_64F).var() if sharpness < sharpness_threshold: return False found, corners = cv2.findChessboardCorners(self.frame, board_dims) self.current_image_points = corners return found
def find_bibs(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY); binary = cv2.GaussianBlur(gray,(5,5),0) ret,binary = cv2.threshold(binary, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU); #binary = cv2.adaptiveThreshold(binary, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) #ret,binary = cv2.threshold(binary, 190, 255, cv2.THRESH_BINARY); #lapl = cv2.Laplacian(image,cv2.CV_64F) #gray = cv2.cvtColor(lapl, cv2.COLOR_BGR2GRAY); #blurred = cv2.GaussianBlur(lapl,(5,5),0) #ret,binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU); #cv2.imwrite("lapl.jpg", lapl) edges = cv2.Canny(image,175,200) cv2.imwrite("edges.jpg", edges) binary = edges cv2.imwrite("binary.jpg", binary) contours,hierarchy = find_contours(binary) return get_rectangles(contours)
def EdgeDetection(img): img = cv2.fastNlMeansDenoising(img,None,3,7,21) _,img = cv2.threshold(img,30,255,cv2.THRESH_TOZERO) denoise_img = img laplacian = cv2.Laplacian(img,cv2.CV_64F) sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5) # x sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3) # y canny = cv2.Canny(img,100,200) contour_image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return {"denoise":denoise_img,"laplacian":laplacian,"canny":canny,"sobely":sobely,"sobelx":sobelx,"contour":contour_image} # GrayScale Image Convertor # https://extr3metech.wordpress.com
def _filter(img, method, k): if method == 'Edge gradient': sy = cv2.Sobel(img, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=k) sx = cv2.Sobel(img, ddepth=cv2.CV_64F,dx=1, dy=0, ksize=k) # sx = sobel(img, axis=0, mode='constant') # sy = sobel(img, axis=1, mode='constant') return np.hypot(sx, sy) if method == 'Sobel-H': return cv2.Sobel(img, ddepth=cv2.CV_64F,dx=0, dy=1, ksize=k) #sobel(img, axis=0, mode='constant') if method == 'Sobel-V': return cv2.Sobel(img, ddepth=cv2.CV_64F,dx=1, dy=0, ksize=k) #sobel(img, axis=1, mode='constant') if method == 'Laplace': return cv2.Laplacian(img, ddepth=cv2.CV_64F,ksize=5) #laplace(img)
def getEdges(gray,detector,min_thr=None,max_thr=None): """ Where detector in {1,2,3,4} 1: Laplacian 2: Sobelx 3: Sobely 4: Canny 5: Sobelx with possitive and negative slope (in 2 negative slopes are lost) """ if min_thr is None: min_thr = 100 max_thr = 200 if detector == 1: return cv2.Laplacian(gray,cv2.CV_64F) elif detector == 2: return cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=-1) elif detector == 3: return cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=-1) elif detector == 4: return cv2.Canny(gray,min_thr,max_thr) # Canny(min_thresh,max_thresh) (threshold not to the intensity but to the # intensity gradient -value that measures how different is a pixel to its neighbors-) elif detector == 5: sobelx64f = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=5) abs_sobel64f = np.absolute(sobelx64f) return np.uint8(abs_sobel64f)
def variance_of_laplacian(im): """ Compute the Laplacian of the image and then return the focus measure, which is simply the variance of the Laplacian http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ """ return cv2.Laplacian(im, cv2.CV_64F).var()
def variance_of_laplacian(image): return cv2.Laplacian(image, cv2.CV_64F).var()
def get_blur(frame, scale): frame = cv2.resize(frame, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) fm = cv2.Laplacian(gray, cv2.CV_64F).var() return fm
def estimate_blur(image, threshold=100): if image.ndim == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blur_map = cv2.Laplacian(image, cv2.CV_64F) score = numpy.var(blur_map) return blur_map, score, bool(score < threshold)
def varianceOfLaplacian(img): ''''LAPV' algorithm (Pech2000)''' lap = cv2.Laplacian(img, ddepth=-1)#cv2.cv.CV_64F) stdev = cv2.meanStdDev(lap)[1] s = stdev[0]**2 return s[0]
def _blur_index(self, img): img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return cv2.Laplacian(img_gray, cv2.CV_64F).var()
def run(self, ips, snap, img, para = None): return cv2.Laplacian(img, -1)
def match_template_mask(image, template, mask=None, method=None, sigma=0.33): """Match template against image applying mask to template using method. Method can be either of (None, 'laplacian', 'sobel', 'scharr', 'prewitt', 'roberts', 'canny'). Returns locations to look for max values.""" if mask is not None: if method: kernel = np.ones((3, 3), np.uint8) mask = cv2.erode(mask, kernel) if method == 'laplacian': # use CV_64F to not loose edges, convert to uint8 afterwards edge_image = np.uint8(np.absolute( cv2.Laplacian(image, cv2.CV_64F))) edge_template = np.uint8(np.absolute( cv2.Laplacian(template, cv2.CV_64F) )) elif method in ('sobel', 'scharr', 'prewitt', 'roberts'): filter_func = getattr(skfilters, method) edge_image = filter_func(image) edge_template = filter_func(template) edge_image = convert(edge_image) edge_template = convert(edge_template) else: # method == 'canny' values = np.hstack([image.ravel(), template.ravel()]) median = np.median(values) lower = int(max(0, (1.0 - sigma) * median)) upper = int(min(255, (1.0 + sigma) * median)) edge_image = cv2.Canny(image, lower, upper) edge_template = cv2.Canny(template, lower, upper) results = cv2.matchTemplate(edge_image, edge_template & mask, cv2.TM_CCOEFF_NORMED) else: results = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED, mask) else: results = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED) return results
def get_frame(self): ret, frame = self.cap.read() laplacian = cv2.Laplacian(frame,cv2.CV_64F) cv2.imwrite('image.jpg',np.hstack((frame,laplacian))) return open('image.jpg', 'rb').read()
def get_frame(self): ret, frame = self.cap.read() laplacian = cv2.Laplacian(frame,cv2.CV_64F) cv2.imwrite('imagewritten.jpg',np.hstack((frame,laplacian))) return open('imagewritten.jpg', 'rb').read()
def analyse_isomaps(self): print ('analysing isomaps...') for example in self.examples_all: img = cv2.imread(example.images[0], cv2.IMREAD_UNCHANGED) #blurryness_map = cv2.Laplacian(img, cv2.CV_64F) #blurryness_map[np.logical_or(blurryness_map<-700, blurryness_map>700)]=0 #try to filter out the edges #example.blurryness = blurryness_map.var() example.blurryness = _get_gradient_magnitude(img) example.coverage = _calc_isomap_coverage(img)
def isomap_playground(): isomaps =[] for i in range(len(isomap_paths)): isomaps.append(cv2.imread(isomap_paths[i], cv2.IMREAD_UNCHANGED)) old_isomap_merged = np.zeros([ISOMAP_SIZE, ISOMAP_SIZE, 4], dtype='uint8') all_isomaps_merged = merge(isomaps) show_isomap('all_isomaps_merged', all_isomaps_merged) #cv2.waitKey() #cv2.destroyAllWindows() #exit() for i in range(len(isomaps)): new_isomap_merged = merge([old_isomap_merged, isomaps[i]]) #blurryness = cv2.Laplacian(isomaps[i], cv2.CV_64F).var() blurryness_map = cv2.Laplacian(isomaps[i], cv2.CV_64F) blurryness_map[np.logical_or(blurryness_map<-700, blurryness_map>700)]=0 #try to filter out the edges blurryness = blurryness_map.var() #show_isomap('laplac',cv2.Laplacian(isomaps[i], cv2.CV_8U)) #print ('max', np.max(cv2.Laplacian(isomaps[i], cv2.CV_64F)), 'min', np.min(cv2.Laplacian(isomaps[i], cv2.CV_64F))) coverage = calc_isomap_coverage(isomaps[i]) print(isomap_paths[i]," isomap coverage:",coverage,"blur detection:",blurryness, "overall score", coverage*coverage*blurryness) show_isomap('new isomap', isomaps[i]) show_isomap('merge', new_isomap_merged) cv2.waitKey() old_isomap_merged = new_isomap_merged #cv2.imwrite('/user/HS204/m09113/Desktop/merge_test.png', isomap_merged) #cv2.waitKey() #cv2.destroyAllWindows()
def EdgeDetection(img): # img = cv2.medianBlur(img,5) img = cv2.fastNlMeansDenoising(img,None,3,7,21) _,img = cv2.threshold(img,30,255,cv2.THRESH_TOZERO) denoise_img = img # print(img) # cv2.imwrite("Denoise.jpg",img) # cv2.waitKey(0) # cv2.destroyAllWindows() # convolute with proper kernels laplacian = cv2.Laplacian(img,cv2.CV_64F) sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5) # x sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3) # y # sobel2y = cv2.Sobel(sobely,cv2.CV_64F,0,1,ksize=3) # sobelxy = cv2.Sobel(img,cv2.CV_64F,1,1,ksize=5) # y canny = cv2.Canny(img,100,200) contour_image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # print(canny) # cv2.imwrite('laplacian.jpg',laplacian) # cv2.imwrite('sobelx.jpg',sobelx) # cv2.imwrite('sobely.jpg',sobely) # cv2.imwrite('sobelxy.jpg',sobelxy) # cv2.imwrite('canny.jpg',canny) # plt.subplot(3,2,1),plt.imshow(img,cmap = 'gray') # plt.title('Original'), plt.xticks([]), plt.yticks([]) # plt.subplot(3,2,2),plt.imshow(laplacian,cmap = 'gray') # plt.title('Laplacian'), plt.xticks([]), plt.yticks([]) # plt.subplot(3,2,3),plt.imshow(sobelx,cmap = 'gray') # plt.title('Sobel X'), plt.xticks([]), plt.yticks([]) # plt.subplot(3,2,4),plt.imshow(sobely,cmap = 'gray') # plt.title('Sobel Y'), plt.xticks([]), plt.yticks([]) # plt.subplot(3,2,4),plt.imshow(sobelxy,cmap = 'gray') # plt.title('Sobel XY'), plt.xticks([]), plt.yticks([]) # plt.subplot(3,2,5),plt.imshow(canny,cmap = 'gray') # plt.title('Canny'), plt.xticks([]), plt.yticks([]) # plt.show() # return {"denoise":img} return {"denoise":denoise_img,"laplacian":laplacian,"canny":canny,"sobely":sobely,"sobelx":sobelx,"contour":contour_image}
def contrast_normalization(image): blurred = cv2.GaussianBlur(image, (3,3), 0) return cv2.Laplacian(blurred, cv2.CV_8U, 3)
def detect(self, image): floatimage = cv2.cvtColor(np.float32(image), cv2.COLOR_BGR2GRAY) / 255 if self.gaussian is None or self.gaussian.shape[0] != Configuration.log_kernel_size: self.gaussian = cv2.getGaussianKernel(Configuration.log_kernel_size, -1, cv2.CV_32F) gaussian_filtered = cv2.sepFilter2D(floatimage, cv2.CV_32F, self.gaussian, self.gaussian) # LoG filtered = cv2.Laplacian(gaussian_filtered, cv2.CV_32F, ksize=Configuration.log_block_size) # DoG #gaussian2 = cv2.getGaussianKernel(Configuration.log_block_size, -1, cv2.CV_32F) #gaussian_filtered2 = cv2.sepFilter2D(floatimage, cv2.CV_32F, gaussian2, gaussian2) #filtered = gaussian_filtered - gaussian_filtered2 mi = np.min(filtered) ma = np.max(filtered) if mi - ma != 0: filtered = 1 - (filtered - mi) / (ma - mi) _, thresholded = cv2.threshold(filtered, Configuration.log_threshold, 1.0, cv2.THRESH_BINARY) self.debug = thresholded thresholded = np.uint8(thresholded) contours = None if int(cv2.__version__.split('.')[0]) == 2: contours, _ = cv2.findContours(thresholded, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) else: _, contours, _ = cv2.findContours(thresholded, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) candidates = [] for i in range(len(contours)): rect = cv2.boundingRect(contours[i]) v1 = rect[0:2] v2 = np.add(rect[0:2], rect[2:4]) if rect[2] < Configuration.log_max_rect_size and rect[3] < Configuration.log_max_rect_size: roi = floatimage[v1[1]:v2[1], v1[0]:v2[0]] _, _, _, maxLoc = cv2.minMaxLoc(roi) maxLoc = np.add(maxLoc, v1) candidates.append(maxLoc) self.candidates = candidates return candidates
