我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用cv2.findContours()。
def __bound_contours(roi): """ returns modified roi(non-destructive) and rectangles that founded by the algorithm. @roi region of interest to find contours @return (roi, rects) """ roi_copy = roi.copy() roi_hsv = cv2.cvtColor(roi, cv2.COLOR_RGB2HSV) # filter black color mask1 = cv2.inRange(roi_hsv, np.array([0, 0, 0]), np.array([180, 255, 125])) mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))) mask1 = cv2.Canny(mask1, 100, 300) mask1 = cv2.GaussianBlur(mask1, (1, 1), 0) mask1 = cv2.Canny(mask1, 100, 300) # mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))) # Find contours for detected portion of the image im2, cnts, hierarchy = cv2.findContours(mask1.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # get largest five contour area rects = [] for c in cnts: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) x, y, w, h = cv2.boundingRect(approx) if h >= 15: # if height is enough # create rectangle for bounding rect = (x, y, w, h) rects.append(rect) cv2.rectangle(roi_copy, (x, y), (x+w, y+h), (0, 255, 0), 1); return (roi_copy, rects)
def find_squares(img): img = cv2.GaussianBlur(img, (5, 5), 0) squares = [] for gray in cv2.split(img): for thrs in xrange(0, 255, 26): if thrs == 0: bin = cv2.Canny(gray, 0, 50, apertureSize=5) bin = cv2.dilate(bin, None) else: retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY) bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: cnt_len = cv2.arcLength(cnt, True) cnt = cv2.approxPolyDP(cnt, 0.02 * cnt_len, True) if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt): cnt = cnt.reshape(-1, 2) max_cos = np.max([angle_cos(cnt[i], cnt[(i + 1) % 4], cnt[(i + 2) % 4]) for i in xrange(4)]) if max_cos < 0.1: squares.append(cnt) return squares
def find_biggest_contour(image): # Copy image = image.copy() #input, gives all the contours, contour approximation compresses horizontal, #vertical, and diagonal segments and leaves only their end points. For example, #an up-right rectangular contour is encoded with 4 points. #Optional output vector, containing information about the image topology. #It has as many elements as the number of contours. #we dont need it _, contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # Isolate largest contour contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours] biggest_contour = max(contour_sizes, key=lambda x: x[0])[1] mask = np.zeros(image.shape, np.uint8) cv2.drawContours(mask, [biggest_contour], -1, 255, -1) return biggest_contour, mask
def find_triangles(filename): FIRST = 0 RED = (0, 0, 255) THICKNESS = 3 copy = img = cv2.imread(filename) grey_img = cv2.imread(file_name, cv2.IMREAD_GRAYSCALE) ret, thresh = cv2.threshold(grey_img, 127, 255, 1) contours, h = cv2.findContours(thresh, 1, 2) largest = None for contour in countours: approx = cv2.approxPolyDP(contour,0.01*cv2.arcLength(contour,True),True) if len(approx) == 3: #triangle found if largest is None or cv2.contourArea(contour) > cv2.contourArea(largest): largest = contour #write file cv2.drawContours(copy, [largest], FIRST, RED, THICKNESS) cv2.imwrite(filename +"_result", copy)
def remove_borders(image): ratio = image.shape[0] / 500.0 orig = image.copy() image = resize(image, height=500) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(gray, 75, 200) _, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) cv2.imshow('edged', edged) cnts = sorted(cnts, key=cv2.contourArea, reverse=True) screenCnt = None for c in cnts: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) print(len(approx) == 4) if len(approx) == 4: screenCnt = approx break cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2) if screenCnt is not None and len(screenCnt) > 0: return four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) return orig
def extract_corners(self, image): """ Find the 4 corners of a binary image :param image: binary image :return: 4 main vertices or None """ cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:] cnt = cnts[0] _, _, h, w = cv2.boundingRect(cnt) epsilon = min(h, w) * 0.5 vertices = cv2.approxPolyDP(cnt, epsilon, True) vertices = cv2.convexHull(vertices, clockwise=True) vertices = self.correct_vertices(vertices) return vertices
def homography(self, img, outdir_name=''): orig = img # 2?????? gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY) gauss = cv2.GaussianBlur(gray, (5, 5), 0) canny = cv2.Canny(gauss, 50, 150) # 2?????????? contours = cv2.findContours(canny, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[1] # ??????????? contours.sort(key=cv2.contourArea, reverse=True) if len(contours) > 0: arclen = cv2.arcLength(contours[0], True) # ??????????? approx = cv2.approxPolyDP(contours[0], 0.01 * arclen, True) # warp = approx.copy() if len(approx) >= 4: self.last_approx = approx.copy() elif self.last_approx is not None: approx = self.last_approx else: approx = self.last_approx rect = self.get_rect_by_points(approx) # warped = self.transform_by4(orig, warp[:, 0, :]) return orig[rect[0]:rect[1], rect[2]:rect[3]]
def find_squares(img, cos_limit = 0.1): print('search for squares with threshold %f' % cos_limit) img = cv2.GaussianBlur(img, (5, 5), 0) squares = [] for gray in cv2.split(img): for thrs in xrange(0, 255, 26): if thrs == 0: bin = cv2.Canny(gray, 0, 50, apertureSize=5) bin = cv2.dilate(bin, None) else: retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY) bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: cnt_len = cv2.arcLength(cnt, True) cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True) if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt): cnt = cnt.reshape(-1, 2) max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)]) if max_cos < cos_limit : squares.append(cnt) else: #print('dropped a square with max_cos %f' % max_cos) pass return squares ### ### Version V2. Collect meta-data along the way, with commentary added. ###
def diff_rect(img1, img2, pos=None): """find counters include pos in differences between img1 & img2 (cv2 images)""" diff = cv2.absdiff(img1, img2) diff = cv2.GaussianBlur(diff, (3, 3), 0) edges = cv2.Canny(diff, 100, 200) _, thresh = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) if not contours: return None contours.sort(key=lambda c: len(c)) # no pos provide, just return the largest different area rect if pos is None: cnt = contours[-1] x0, y0, w, h = cv2.boundingRect(cnt) x1, y1 = x0+w, y0+h return (x0, y0, x1, y1) # else the rect should contain the pos x, y = pos for i in range(len(contours)): cnt = contours[-1-i] x0, y0, w, h = cv2.boundingRect(cnt) x1, y1 = x0+w, y0+h if x0 <= x <= x1 and y0 <= y <= y1: return (x0, y0, x1, y1)
def find_contours(self, img): thresh_img = self.threshold(img) _, contours, _ = cv2.findContours(thresh_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) result = [] for cnt in contours: approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True) if self.draw_approx: cv2.drawContours(self.out, [approx], -1, self.BLUE, 2, lineType=8) if len(approx) > 3 and len(approx) < 15: _, _, w, h = cv2.boundingRect(approx) if h > self.min_height and w > self.min_width: hull = cv2.convexHull(cnt) approx2 = cv2.approxPolyDP(hull,0.01*cv2.arcLength(hull,True),True) if self.draw_approx2: cv2.drawContours(self.out, [approx2], -1, self.GREEN, 2, lineType=8) result.append(approx2) return result
def find_center(self, name, frame, mask, min_radius): if name not in self.pts: self.pts[name] = deque(maxlen=self.params['tracking']['buffer_size']) # find contours in the mask and initialize the current (x, y) center of the ball cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] # only proceed if at least one contour was found if len(cnts) > 0: # find the largest contour in the mask, then use it to compute the minimum enclosing circle and centroid c = max(cnts, key=cv2.contourArea) ((x, y), radius) = cv2.minEnclosingCircle(c) center = (int(x), int(y)) # only proceed if the radius meets a minimum size if radius > min_radius: # draw the circle and centroid on the frame, then update the list of tracked points cv2.circle(frame, center, int(radius), (0, 255, 255), 2) cv2.circle(frame, center, 5, (0, 0, 255), -1) self.pts[name].appendleft(center) smooth_points = 8 return (int(np.mean([self.pts[name][i][0] for i in range(min(smooth_points, len(self.pts[name])))])), int(np.mean([self.pts[name][i][1] for i in range(min(smooth_points, len(self.pts[name])))]))), radius return None, None
def classify(img): cimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img2 = cv2.medianBlur(cimg, 13) ret, thresh1 = cv2.threshold(cimg, 100, 120, cv2.THRESH_BINARY) t2 = copy.copy(thresh1) x, y = thresh1.shape arr = np.zeros((x, y, 3), np.uint8) final_contours = [] image, contours, hierarchy = cv2.findContours(t2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #cv2.imshow('image', image) #k = cv2.waitKey(0) for i in range(len(contours)): cnt = contours[i] if cv2.contourArea(cnt) > 3000 and cv2.contourArea(cnt) < 25000: cv2.drawContours(img, [cnt], -1, [0, 255, 255]) cv2.fillConvexPoly(arr, cnt, [255, 255, 255]) final_contours.append(cnt) #cv2.imshow('arr', arr) #k = cv2.waitKey(0) return arr
def diagContour(image): #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black image.fill(0) boundingRect = [] firstFail = [] #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing for j in [i for i in contours if cv2.contourArea(i) > minArea]: #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner br = cv2.boundingRect(j) if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol): boundingRect.append(br) else: firstFail.append([br, br[2]/br[3], cv2.contourArea(j)/(br[2]*br[3])]) secondRound = [] for x in range(0, len(boundingRect)): for y in range(x+1, len(boundingRect)): i = boundingRect[x] j = boundingRect[y] secondRound.append([(x,y,i,j), (abs(i[1]-j[1]), i[3]/2), abs(i[0]-j[0])/i[1]]) for x in secondRound: if(x[1][0] < x[1][1] and x[2] - GRPASPECT < grpAspectTol): return [x[0][2], x[0][3]] return None;
def findEllipses(edges): contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) ellipseMask = np.zeros(edges.shape, dtype=np.uint8) contourMask = np.zeros(edges.shape, dtype=np.uint8) pi_4 = np.pi * 4 for i, contour in enumerate(contours): if len(contour) < 5: continue area = cv2.contourArea(contour) if area <= 100: # skip ellipses smaller then 10x10 continue arclen = cv2.arcLength(contour, True) circularity = (pi_4 * area) / (arclen * arclen) ellipse = cv2.fitEllipse(contour) poly = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])), (int(ellipse[1][0] / 2), int(ellipse[1][1] / 2)), int(ellipse[2]), 0, 360, 5) # if contour is circular enough if circularity > 0.6: cv2.fillPoly(ellipseMask, [poly], 255) continue # if contour has enough similarity to an ellipse similarity = cv2.matchShapes(poly.reshape((poly.shape[0], 1, poly.shape[1])), contour, cv2.cv.CV_CONTOURS_MATCH_I2, 0) if similarity <= 0.2: cv2.fillPoly(contourMask, [poly], 255) return ellipseMask, contourMask
def extract_corners(self, image): """ Find the 4 corners of a binary image :param image: binary image :return: 4 main vertices or None """ cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:] cnt = cnts[0] _, _, h, w = cv2.boundingRect(cnt) epsilon = min(h, w) * 0.5 o_vertices = cv2.approxPolyDP(cnt, epsilon, True) vertices = cv2.convexHull(o_vertices, clockwise=True) vertices = self.correct_vertices(vertices) if self.debug: temp = cv2.cvtColor(image.copy(), cv2.COLOR_GRAY2BGR) cv2.drawContours(temp, cnts, -1, (0, 255, 0), 10) cv2.drawContours(temp, o_vertices, -1, (255, 0, 0), 30) cv2.drawContours(temp, vertices, -1, (0, 0, 255), 20) self.save2image(temp) return vertices
def imgSeg_contour(img, b,g,r, per): lower = np.array([0, 0, 0]) upper = np.array([b,g,r]) shapeMask = cv2.inRange(img, lower, upper) #http://stackoverflow.com/questions/27746089/python-computer-vision-contours-too-many-values-to-unpack _, cnts, hierarchy = cv2.findContours(shapeMask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:4] for c in cnts: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, per * peri, True) ### 0.04 ### if (len(approx) >= 4) and (len(approx) < 6): break return approx
def shapeFiltering(img): contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0] if len(contours) == 0: print "shapeFiltering: Error, no contours found" return 1 good_shape = [] for c in contours: x,y,w,h = cv2.boundingRect(c) #if h == 0: # continue ratio = w / h ratio_grade = ratio / (TMw / TMh) if 0.2 < ratio_grade < 1.8: good_shape.append(c) return good_shape
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 find_contour(self, img_src, Rxmin, Rymin, Rxmax, Rymax): cv2.rectangle(img_src, (Rxmax, Rymax), (Rxmin, Rymin), (0, 255, 0), 0) crop_res = img_src[Rymin: Rymax, Rxmin:Rxmax] grey = cv2.cvtColor(crop_res, cv2.COLOR_BGR2GRAY) _, thresh1 = cv2.threshold(grey, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) cv2.imshow('Thresh', thresh1) contours, hierchy = cv2.findContours(thresh1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # draw contour on threshold image if len(contours) > 0: cv2.drawContours(thresh1, contours, -1, (0, 255, 0), 3) return contours, crop_res # Check ConvexHull and Convexity Defects
def polygonize_cv(mask, epsilon=1., min_area=10.): contours, hierarchy = cv2.findContours(mask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # create approximate contours to have reasonable submission size approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours] approx_contours = contours if not contours: return MultiPolygon() # now messy stuff to associate parent and child contours cnt_children = defaultdict(list) child_contours = set() assert hierarchy.shape[0] == 1 # http://docs.opencv.org/3.1.0/d9/d8b/tutorial_py_contours_hierarchy.html for idx, (_, _, _, parent_idx) in enumerate(hierarchy[0]): if parent_idx != -1: child_contours.add(idx) cnt_children[parent_idx].append(approx_contours[idx]) # create actual polygons filtering by area (removes artifacts) all_polygons = [] for idx, cnt in enumerate(approx_contours): if idx not in child_contours and cv2.contourArea(cnt) >= min_area: assert cnt.shape[1] == 1 poly = Polygon( shell=cnt[:, 0, :], holes=[c[:, 0, :] for c in cnt_children.get(idx, []) if cv2.contourArea(c) >= min_area]) all_polygons.append(poly) # approximating polygons might have created invalid ones, fix them all_polygons = MultiPolygon(all_polygons) if not all_polygons.is_valid: all_polygons = all_polygons.buffer(0) # Sometimes buffer() converts a simple Multipolygon to just a Polygon, # need to keep it a Multi throughout if all_polygons.type == 'Polygon': all_polygons = MultiPolygon([all_polygons]) return all_polygons
def process_img(img): original_image=img processed_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300) processed_img = cv2.GaussianBlur(processed_img, (3,3), 0 ) copy=processed_img vertices = np.array([[30, 240], [30, 100], [195, 100], [195, 240]]) processed_img = roi(processed_img, np.int32([vertices])) verticesP = np.array([[30, 270], [30, 230], [197, 230], [197, 270]]) platform = roi(copy, np.int32([verticesP])) # edges #lines = cv2.HoughLinesP(platform, 1, np.pi/180, 180,np.array([]), 3, 2) #draw_lines(processed_img,lines) #draw_lines(original_image,lines) #Platform lines #imgray = cv2.cvtColor(platform,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(platform,127,255,0) im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(original_image, contours, -1, (0,255,0), 3) try: platformpos=contours[0][0][0] except: platformpos=[[0]] circles = cv2.HoughCircles(processed_img, cv2.HOUGH_GRADIENT, 1, 20, param1=90, param2=5, minRadius=1, maxRadius=3) ballpos=draw_circles(original_image,circles=circles) return processed_img,original_image,platform,platformpos,ballpos
def cannyThresholding(self, contour_retrieval_mode = cv2.RETR_LIST): ''' contour_retrieval_mode is passed through as second argument to cv2.findContours ''' # Attempt to match edges found in blue, green or red channels : collect all channel = 0 for gray in cv2.split(self.img): channel += 1 print('channel %d ' % channel) title = self.tgen.next('channel-%d' % channel) if self.show: ImageViewer(gray).show(window = title, destroy = self.destroy, info = self.info, thumbnailfn = title) found = {} for thrs in xrange(0, 255, 26): print('Using threshold %d' % thrs) if thrs == 0: print('First step') bin = cv2.Canny(gray, 0, 50, apertureSize=5) title = self.tgen.next('canny-%d' % channel) if self.show: ImageViewer(bin).show(window = title, destroy = self.destroy, info = self.info, thumbnailfn = title) bin = cv2.dilate(bin, None) title = self.tgen.next('canny-dilate-%d' % channel) if self.show: ImageViewer(bin).show(window = title, destroy = self.destroy, info = self.info, thumbnailfn = title) else: retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY) title = self.tgen.next('channel-%d-threshold-%d' % (channel, thrs)) if self.show: ImageViewer(bin).show(window='Next threshold (n to continue)', destroy = self.destroy, info = self.info, thumbnailfn = title) bin, contours, hierarchy = cv2.findContours(bin, contour_retrieval_mode, cv2.CHAIN_APPROX_SIMPLE) title = self.tgen.next('channel-%d-threshold-%d-contours' % (channel, thrs)) if self.show: ImageViewer(bin).show(window = title, destroy = self.destroy, info = self.info, thumbnailfn = title) if contour_retrieval_mode == cv2.RETR_LIST or contour_retrieval_mode == cv2.RETR_EXTERNAL: filteredContours = contours else: filteredContours = [] h = hierarchy[0] for component in zip(contours, h): currentContour = component[0] currentHierarchy = component[1] if currentHierarchy[3] < 0: # Found the outermost parent component filteredContours.append(currentContour) print('Contours filtered. Input %d Output %d' % (len(contours), len(filteredContours))) time.sleep(5) for cnt in filteredContours: cnt_len = cv2.arcLength(cnt, True) cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True) cnt_len = len(cnt) cnt_area = cv2.contourArea(cnt) cnt_isConvex = cv2.isContourConvex(cnt) if cnt_len == 4 and (cnt_area > self.area_min and cnt_area < self.area_max) and cnt_isConvex: cnt = cnt.reshape(-1, 2) max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)]) if max_cos < self.cos_limit : sq = Square(cnt, cnt_area, cnt_isConvex, max_cos) self.squares.append(sq) else: #print('dropped a square with max_cos %f' % max_cos) pass found[thrs] = len(self.squares) print('Found %d quadrilaterals with threshold %d' % (len(self.squares), thrs))
def find_chars(img): gray = np.array(img.convert("L")) ret, mask = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY) image_final = cv2.bitwise_and(gray, gray, mask=mask) ret, new_img = cv2.threshold(image_final, 180, 255, cv2.THRESH_BINARY_INV) kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)) dilated = cv2.dilate(new_img, kernel, iterations=1) # Image.fromarray(dilated).save('out.png') # for debugging _, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) coords = [] for contour in contours: # get rectangle bounding contour [x, y, w, h] = cv2.boundingRect(contour) # ignore large chars (probably not chars) if w > 70 and h > 70: continue coords.append((x, y, w, h)) return coords # find list of eye coordinates in image
def cropCircle(img, resize=None): if resize: if (img.shape[0] > img.shape[1]): tile_size = (int(img.shape[1] * resize / img.shape[0]), resize) else: tile_size = (resize, int(img.shape[0] * resize / img.shape[1])) img = cv2.resize(img, dsize=tile_size, interpolation=cv2.INTER_CUBIC) else: tile_size = img.shape gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY); _, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY) _, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) main_contour = sorted(contours, key=cv2.contourArea, reverse=True)[0] ff = np.zeros((gray.shape[0], gray.shape[1]), 'uint8') cv2.drawContours(ff, main_contour, -1, 1, 15) ff_mask = np.zeros((gray.shape[0] + 2, gray.shape[1] + 2), 'uint8') cv2.floodFill(ff, ff_mask, (int(gray.shape[1] / 2), int(gray.shape[0] / 2)), 1) rect = maxRect(ff) rectangle = [min(rect[0], rect[2]), max(rect[0], rect[2]), min(rect[1], rect[3]), max(rect[1], rect[3])] img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]] cv2.rectangle(ff, (min(rect[1], rect[3]), min(rect[0], rect[2])), (max(rect[1], rect[3]), max(rect[0], rect[2])), 3, 2) return [img_crop, rectangle, tile_size]
def test_initial_pass_through_compare(self): original = cv2.imread(os.path.join(self.provider.assets, "start_screen.png")) against = self.provider.get_img_from_screen_shot() wrong = cv2.imread(os.path.join(self.provider.assets, "battle.png")) # convert the images to grayscale original = mask_image([127], [255], cv2.cvtColor(original, cv2.COLOR_BGR2GRAY), True) against = mask_image([127], [255], cv2.cvtColor(against, cv2.COLOR_BGR2GRAY), True) wrong = mask_image([127], [255], cv2.cvtColor(wrong, cv2.COLOR_BGR2GRAY), True) # initialize the figure (score, diff) = compare_ssim(original, against, full=True) diff = (diff * 255).astype("uint8") self.assertTrue(score > .90, 'If this is less then .90 the initial compare of the app will fail') (score, nothing) = compare_ssim(original, wrong, full=True) self.assertTrue(score < .90) if self.__debug_pictures__: # threshold the difference image, followed by finding contours to # obtain the regions of the two input images that differ thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] # loop over the contours for c in cnts: # compute the bounding box of the contour and then draw the # bounding box on both input images to represent where the two # images differ (x, y, w, h) = cv2.boundingRect(c) cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2) cv2.rectangle(against, (x, y), (x + w, y + h), (0, 0, 255), 2) # show the output images diffs = ("Original", original), ("Modified", against), ("Diff", diff), ("Thresh", thresh) images = ("Original", original), ("Against", against), ("Wrong", wrong) self.setup_compare_images(diffs) self.setup_compare_images(images)
def getContours(img,kernel=(10,10)): #Define kernel kernel = np.ones(kernel, np.uint8) #Open to erode small patches thresh = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) #Close little holes thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE,kernel, iterations=4) #Find contours #contours=skimsr.find_contours(thresh,0) thresh=thresh.astype('uint8') contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) areas=[] for c in contours: areas.append(cv2.contourArea(c)) return contours,thresh,areas
def findSignificantContours(img, sobel_8u, sobel): image, contours, heirarchy = cv2.findContours(sobel_8u, \ cv2.RETR_EXTERNAL, \ cv2.CHAIN_APPROX_SIMPLE) mask = np.ones(image.shape[:2], dtype="uint8") * 255 level1 = [] for i, tupl in enumerate(heirarchy[0]): if tupl[3] == -1: tupl = np.insert(tupl, 0, [i]) level1.append(tupl) significant = [] tooSmall = sobel_8u.size * 10 / 100 for tupl in level1: contour = contours[tupl[0]]; area = cv2.contourArea(contour) if area > tooSmall: cv2.drawContours(mask, \ [contour], 0, (0, 255, 0), \ 2, cv2.LINE_AA, maxLevel=1) significant.append([contour, area]) significant.sort(key=lambda x: x[1]) significant = [x[0] for x in significant]; peri = cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, 0.02 * peri, True) mask = sobel.copy() mask[mask > 0] = 0 cv2.fillPoly(mask, significant, 255, 0) mask = np.logical_not(mask) img[mask] = 0; return img
def draw_contours(self): """""" # contours all the objects found # (findContours changes the source image, # hence copy) contours, _ = cv2.findContours(self.mask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # rectangles for contour in contours: size = cv2.contourArea(contour) if size > self.threshold: # only larger objects ret_x, ret_y, ret_w, ret_h = cv2.boundingRect(contour) cv2.rectangle(self.display, (ret_x, ret_y), (ret_x+ret_w, ret_y+ret_h), (0, 255, 255), 2)
def find_contours(mask, smooth_factor=0.005): """ Find the contours in a given mask """ border = 5 # Canny detection breaks down with the edge of the image my_mask = cv2.copyMakeBorder(mask, border, border, border, border, cv2.BORDER_CONSTANT, value=(0, 0, 0)) my_mask = cv2.cvtColor(my_mask, cv2.COLOR_BGR2GRAY) if is_cv2(): contours, _ = cv2.findContours(my_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) else: _, contours, _ = cv2.findContours(my_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # shift the contours back down for contour in contours: for pnt in contour: if pnt[0][1] > border: pnt[0][1] = pnt[0][1] - border else: pnt[0][1] = 0 if pnt[0][0] > border: pnt[0][0] = pnt[0][0] - border else: pnt[0][0] = 0 closed_contours = [] for contour in contours: epsilon = smooth_factor*cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) area = cv2.contourArea(approx) # if they are too small they are not edges if area < 200: continue closed_contours.append(approx) return closed_contours
def find_components(im, max_components=16): """Dilate the image until there are just a few connected components. Returns contours for these components.""" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)) dilation = dilate(im, kernel, 6) count = 21 n = 0 sigma = 0.000 while count > max_components: n += 1 sigma += 0.005 result = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if len(result) == 3: _, contours, hierarchy = result elif len(result) == 2: contours, hierarchy = result possible = find_likely_rectangles(contours, sigma) count = len(possible) return (dilation, possible, n)
def extract_rect(im): imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(imgray, 127, 255, 0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # finding contour with max area largest = None for cnt in contours: if largest == None or cv2.contourArea(cnt) > cv2.contourArea(largest): largest = cnt peri = cv2.arcLength(largest, True) appr = cv2.approxPolyDP(largest, 0.02 * peri, True) #cv2.drawContours(im, appr, -1, (0,255,0), 3) points_list = [[i[0][0], i[0][1]] for i in appr] left = sorted(points_list, key = lambda p: p[0])[0:2] right = sorted(points_list, key = lambda p: p[0])[2:4] print("l " + str(left)) print("r " + str(right)) lu = sorted(left, key = lambda p: p[1])[0] ld = sorted(left, key = lambda p: p[1])[1] ru = sorted(right, key = lambda p: p[1])[0] rd = sorted(right, key = lambda p: p[1])[1] print("lu " + str(lu)) print("ld " + str(ld)) print("ru " + str(ru)) print("rd " + str(rd)) lu_ = [ (lu[0] + ld[0])/2, (lu[1] + ru[1])/2 ] ld_ = [ (lu[0] + ld[0])/2, (ld[1] + rd[1])/2 ] ru_ = [ (ru[0] + rd[0])/2, (lu[1] + ru[1])/2 ] rd_ = [ (ru[0] + rd[0])/2, (ld[1] + rd[1])/2 ] print("lu_ " + str(lu_)) print("ld_ " + str(ld_)) print("ru_ " + str(ru_)) print("rd_ " + str(rd_)) src_pts = np.float32(np.array([lu, ru, rd, ld])) dst_pts = np.float32(np.array([lu_, ru_, rd_, ld_])) h,w,b = im.shape H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) print("H" + str(H)) imw = cv2.warpPerspective(im, H, (w, h)) return imw[lu_[1]:rd_[1], lu_[0]:rd_[0]] # cropping image
def recognize_text(original): idcard = original gray = cv2.cvtColor(idcard, cv2.COLOR_BGR2GRAY) # Morphological gradient: kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) opening = cv2.morphologyEx(gray, cv2.MORPH_GRADIENT, kernel) # Binarization ret, binarization = cv2.threshold(opening, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Connected horizontally oriented regions kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1)) connected = cv2.morphologyEx(binarization, cv2.MORPH_CLOSE, kernel) # find countours _, contours, hierarchy = cv2.findContours( connected, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE ) return contours, hierarchy
def find_components(edges, max_components=16): """Dilate the image until there are just a few connected components. Returns contours for these components.""" # Perform increasingly aggressive dilation until there are just a few # connected components. count = 21 dilation = 5 n = 1 while count > 16: n += 1 dilated_image = dilate(edges, N=3, iterations=n) _, contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) count = len(contours) #print dilation #Image.fromarray(edges).show() #Image.fromarray(255 * dilated_image).show() return contours
def _find_size_candidates(self, image): binary_image = self._filter_image(image) _, contours, _ = cv2.findContours(binary_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) size_candidates = [] for contour in contours: bounding_rect = cv2.boundingRect(contour) contour_area = cv2.contourArea(contour) if self._is_valid_contour(contour_area, bounding_rect): candidate = (bounding_rect[2] + bounding_rect[3]) / 2 size_candidates.append(candidate) return size_candidates
def camera_gesture_trigger(): # Capture frame-by-frame ret, frame = cap.read() # Our operations on the frame come here gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray,(5,5),0) ret,thresh1 = cv2.threshold(blur,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) contours, hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) max_area=0 for i in range(len(contours)): cnt=contours[i] area = cv2.contourArea(cnt) if(area>max_area): max_area=area ci=i cnt=contours[ci] hull = cv2.convexHull(cnt) moments = cv2.moments(cnt) cnt = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True) hull = cv2.convexHull(cnt,returnPoints = False) defects = cv2.convexityDefects(cnt,hull) if defects is not None: if defects.shape[0] >= 5: return 1 return 0
def get_largest(im, n): # Find contours of the shape major = cv2.__version__.split('.')[0] if major == '3': _, contours, _ = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) else: contours, _ = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Cycle through contours and add area to array areas = [] for c in contours: areas.append(cv2.contourArea(c)) # Sort array of areas by size sorted_areas = sorted(zip(areas, contours), key=lambda x: x[0], reverse=True) if sorted_areas and len(sorted_areas) >= n: # Find nth largest using data[n-1][1] return sorted_areas[n - 1][1] else: return None
def classify(img): cimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img2 = cv2.medianBlur(cimg, 13) ret, thresh1 = cv2.threshold(cimg, 100, 120, cv2.THRESH_BINARY) t2 = copy.copy(thresh1) x, y = thresh1.shape arr = np.zeros((x, y, 3), np.uint8) final_contours = [] image, contours, hierarchy = cv2.findContours(t2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #cv2.imshow('image', image) #k = cv2.waitKey(0) for i in range(len(contours)): cnt = contours[i] if cv2.contourArea(cnt) > 35000 and cv2.contourArea(cnt) < 15000: cv2.drawContours(img, [cnt], -1, [0, 255, 255]) cv2.fillConvexPoly(arr, cnt, [255, 255, 255]) final_contours.append(cnt) cv2.imshow('arr', arr) k = cv2.waitKey(0) return arr
def get_image_xy_corner(self): """get ?artesian coordinates from raster""" import cv2 if not self.image_path: return False image_xy_corners = [] img = cv2.imread(self.image_path, cv2.IMREAD_GRAYSCALE) imagem = (255 - img) try: ret, thresh = cv2.threshold(imagem, 10, 128, cv2.THRESH_BINARY) try: contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) except Exception: im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) hierarchy = hierarchy[0] hierarhy_contours = [[] for _ in range(len(hierarchy))] for fry in range(len(contours)): currentContour = contours[fry] currentHierarchy = hierarchy[fry] cc = [] # epsilon = 0.0005 * cv2.arcLength(contours[len(contours) - 1], True) approx = cv2.approxPolyDP(currentContour, self.epsilon, True) if len(approx) > 2: for c in approx: cc.append([c[0][0], c[0][1]]) parent_index = currentHierarchy[3] index = fry if parent_index < 0 else parent_index hierarhy_contours[index].append(cc) image_xy_corners = [c for c in hierarhy_contours if len(c) > 0] return image_xy_corners except Exception as ex: self.error(ex) return image_xy_corners
def _detect_bot(self, hsv_image): BOT_MIN = np.array([28,8,100], np.uint8) BOT_MAX = np.array([32,255,255], np.uint8) thresholded_image = cv2.inRange(hsv_image, BOT_MIN, BOT_MAX) thresholded_image = cv2.medianBlur(thresholded_image, 15) _, contours, hierarchy = cv2.findContours(thresholded_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if not contours: (bot_x, bot_y) = (-1000,-1000) else: bot = contours[0] M = cv2.moments(bot) if len(bot) > 2: bot_x = int(M['m10']/M['m00']) bot_y = int(M['m01']/M['m00']) else: (bot_x, bot_y) = (-1000,-1000) return thresholded_image, (bot_x, bot_y)
def remove_blobs(image, min_area=0, max_area=sys.maxsize, threshold=128, method='8-connected', return_mask=False): """Binarize image using threshold, and remove (turn into black) blobs of connected pixels of white of size bigger or equal than min_area but smaller or equal than max_area from the original image, returning it afterward.""" method = method.lower() if method == '4-connected': method = cv2.LINE_4 elif method in ('16-connected', 'antialiased'): method = cv2.LINE_AA else: # 8-connected method = cv2.LINE_8 mono_image = binarize_image(image, method='boolean', threshold=threshold) _, all_contours, _ = cv2.findContours(mono_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) contours = np.array([contour for contour in all_contours if min_area <= cv2.contourArea(contour) <= max_area]) mask = np.ones(mono_image.shape, np.uint8) cv2.drawContours(mask, contours, -1, 0, -1, lineType=method) return image, 255 * mask
def contourImg(image): #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black image.fill(0) boundingRect = [] #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing for j in [i for i in contours if cv2.contourArea(i) > minArea]: #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner br = cv2.boundingRect(j) if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol): boundingRect.append(br) for x in range(0, len(boundingRect)): for y in range(x+1, len(boundingRect)): i = boundingRect[x] j = boundingRect[y] if(abs(i[1]-j[1]) < i[3]/2) and abs(abs(i[0]-j[0])/i[1] - GRPASPECT) < grpAspectTol: return [createRectCnt(i), createRectCnt(j)] return None
def diagContour(image): #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black image.fill(0) boundingRect = [] firstFail = [] #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing for j in [i for i in contours if cv2.contourArea(i) > minArea]: #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner br = cv2.boundingRect(j) if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol): boundingRect.append(br) else: firstFail.append([br, br[2]/br[3], cv2.contourArea(j)/(br[2]*br[3])]) secondRound = [] for x in range(0, len(boundingRect)): for y in range(x+1, len(boundingRect)): i = boundingRect[x] j = boundingRect[y] secondRound.append([(x,y,i,j), (abs(i[1]-j[1]), i[3]/2), abs(i[0]-j[0])/i[1]]) for x in secondRound: if(x[1][0] < x[1][1] and x[2] - GRPASPECT < grpAspectTol): return firstFail, secondRound, [createRectCnt(x[0][2]), createRectCnt(x[0][3])] return firstFail, secondRound, None
def plateDetect(img,img2): '''?????????????????''' im2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for con in contours: x,y,w,h=cv2.boundingRect(con) area=w*h ratio=w/h if ratio>2 and ratio<4 and area>=2000 and area<=25000: logo_y1=max(0,int(y-h*3.0)) logo_y2=y logo_x1=x logo_x2=x+w img_logo=img2.copy() logo=img_logo[logo_y1:logo_y2,logo_x1:logo_x2] cv2.imwrite('./logo1.jpg',logo) cv2.rectangle(img2,(x,y),(x+w,y+h),(255,0,0),2) cv2.rectangle(img2,(logo_x1,logo_y1),(logo_x2,logo_y2),(0,255,0),2) global plate plate=[x,y,w,h] #????????? return logo
def logoDetect(img,imgo): '''???????????????''' imglogo=imgo.copy() img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img=cv2.resize(img,(2*img.shape[1],2*img.shape[0]),interpolation=cv2.INTER_CUBIC) #img=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,-3) ret,img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) #img=cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize = 9) img=cv2.Canny(img,100,200) element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) img = cv2.dilate(img, element2,iterations = 1) img = cv2.erode(img, element1, iterations = 3) img = cv2.dilate(img, element2,iterations = 3) #???? im2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) tema=0 result=[] for con in contours: x,y,w,h=cv2.boundingRect(con) area=w*h ratio=max(w/h,h/w) if area>300 and area<20000 and ratio<2: if area>tema: tema=area result=[x,y,w,h] ratio2=ratio #?????????????????,?????????? logo2_X=[int(result[0]/2+plate[0]-3),int(result[0]/2+plate[0]+result[2]/2+3)] logo2_Y=[int(result[1]/2+max(0,plate[1]-plate[3]*3.0)-3),int(result[1]/2+max(0,plate[1]-plate[3]*3.0)+result[3]/2)+3] cv2.rectangle(img,(result[0],result[1]),(result[0]+result[2],result[1]+result[3]),(255,0,0),2) cv2.rectangle(imgo,(logo2_X[0],logo2_Y[0]),(logo2_X[1],logo2_Y[1]),(0,0,255),2) print tema,ratio2,result logo2=imglogo[logo2_Y[0]:logo2_Y[1],logo2_X[0]:logo2_X[1]] cv2.imwrite('./logo2.jpg',logo2) return img
def apply(self, item, threshold): img = item.img_mat img[img > 0] = 255 _, contours, _ = cv2.findContours(img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) valid_contours = [] for contour in contours: max_x = contour[:, :, 0].max() min_x = contour[:, :, 0].min() max_y = contour[:, :, 1].max() min_y = contour[:, :, 1].min() if (max_x - min_x) * (max_y - min_y) > threshold: valid_contours.append(contour) yield valid_contours
def find_components(edges, max_components=16): """Dilate the image until there are just a few connected components. Returns contours for these components.""" # Perform increasingly aggressive dilation until there are just a few # connected components. count = 21 dilation = 5 n = 1 while count > 16: n += 1 dilated_image = dilate(edges, N=3, iterations=n) contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) count = len(contours) #print dilation #Image.fromarray(edges).show() #Image.fromarray(255 * dilated_image).show() return contours
def findContours(arg_img,arg_canvas, arg_MinMaxArea=False, arg_debug= False): image= arg_img.copy() #print image canvas= arg_canvas.copy() if len(image)==3: image = cv2.cvtColor(self.image, cv2.COLOR_GRAY2BGR) if sys.version_info.major == 2: ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) else: _, ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if arg_MinMaxArea is not False: ctrs = filter(lambda x : arg_MinMaxArea[1]> cv2.contourArea(x) > arg_MinMaxArea[0] , ctrs) print '>>> ', len(ctrs) for ctr in ctrs: print 'Area: ', cv2.contourArea(ctr) cv2.drawContours(canvas, [ctr], 0, (0, 128, 255), 3) if arg_debug: cv2.imwrite('Debug/debug_findContours.jpg',canvas) return canvas
def get_contour(self, arg_frame, arg_export_index, arg_export_path, arg_export_filename, arg_binaryMethod): # Otsu's thresholding after Gaussian filtering tmp = cv2.cvtColor(arg_frame, cv2.COLOR_RGB2GRAY) blur = cv2.GaussianBlur(tmp,(5,5),0) if arg_binaryMethod== 0: ret, thresholdedImg= cv2.threshold(blur.copy() , self.threshold_graylevel, 255 , 0) elif arg_binaryMethod == 1: ret,thresholdedImg = cv2.threshold(blur.copy(),0 ,255 ,cv2.THRESH_BINARY+cv2.THRESH_OTSU) elif arg_binaryMethod== 2: thresholdedImg = cv2.adaptiveThreshold(blur.copy(),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,0) result = cv2.cvtColor(thresholdedImg, cv2.COLOR_GRAY2RGB) ctrs, hier = cv2.findContours(thresholdedImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) ctrs = filter(lambda x : cv2.contourArea(x) > self.threshold_size , ctrs) rects = [[cv2.boundingRect(ctr) , ctr] for ctr in ctrs] for rect , cntr in rects: cv2.drawContours(result, [cntr], 0, (0, 128, 255), 3) if arg_export_index: cv2.imwrite(arg_export_path+ arg_export_filename+'.jpg', result) print "Get Contour success" return result
