我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用cv2.pointPolygonTest()。
def mark_hand_center(frame_in,cont): max_d=0 pt=(0,0) x,y,w,h = cv2.boundingRect(cont) for ind_y in xrange(int(y+0.3*h),int(y+0.8*h)): #around 0.25 to 0.6 region of height (Faster calculation with ok results) for ind_x in xrange(int(x+0.3*w),int(x+0.6*w)): #around 0.3 to 0.6 region of width (Faster calculation with ok results) dist= cv2.pointPolygonTest(cont,(ind_x,ind_y),True) if(dist>max_d): max_d=dist pt=(ind_x,ind_y) if(max_d>radius_thresh*frame_in.shape[1]): thresh_score=True cv2.circle(frame_in,pt,int(max_d),(255,0,0),2) else: thresh_score=False return frame_in,pt,max_d,thresh_score # 6. Find and display gesture
def blendImages(src, dst, mask, featherAmount=0.2): #indeksy nie czarnych pikseli maski maskIndices = np.where(mask != 0) #te same indeksy tylko, ze teraz w jednej macierzy, gdzie kazdy wiersz to jeden piksel (x, y) maskPts = np.hstack((maskIndices[1][:, np.newaxis], maskIndices[0][:, np.newaxis])) faceSize = np.max(maskPts, axis=0) - np.min(maskPts, axis=0) featherAmount = featherAmount * np.max(faceSize) hull = cv2.convexHull(maskPts) dists = np.zeros(maskPts.shape[0]) for i in range(maskPts.shape[0]): dists[i] = cv2.pointPolygonTest(hull, (maskPts[i, 0], maskPts[i, 1]), True) weights = np.clip(dists / featherAmount, 0, 1) composedImg = np.copy(dst) composedImg[maskIndices[0], maskIndices[1]] = weights[:, np.newaxis] * src[maskIndices[0], maskIndices[1]] + (1 - weights[:, np.newaxis]) * dst[maskIndices[0], maskIndices[1]] return composedImg #uwaga, tutaj src to obraz, z ktorego brany bedzie kolor
def find_intersect(image, contours, row, direction, center_col=None): """ Find the intersection from a given centerline to the first contours to the left and right """ if center_col is not None: col = center_col else: col = image.shape[1] / 2 intersect = None i_contour = None while intersect is None: for i, contour in enumerate(contours): if cv2.pointPolygonTest(contour, (col, row), False) >= 0: intersect = col i_contour = i break col = col + direction if col < 0 or col > image.shape[1]: break return i_contour, intersect
def forward_intersect(image, contours, center_col=None): """ Find if there is a contour intersect forward """ if center_col is not None: col = center_col else: col = image.shape[1] / 2 intersect = None i_contour = None row = 0 while intersect is None: for i, contour in enumerate(contours): if cv2.pointPolygonTest(contour, (col, row), False) >= 0: intersect = row i_contour = i break row += 1 if row > image.shape[0]: break if intersect is None: intersect = image.shape[0] return {'contour': i_contour, 'distance': intersect}
def CloseInContour( mask, element ): large = 0 result = mask _, contours, _ = cv2.findContours(result,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #find the biggest area c = max(contours, key = cv2.contourArea) closing = cv2.morphologyEx(result, cv2.MORPH_CLOSE, element) for x in range(mask.shape[0]): for y in range(mask.shape[1]): pt = cv2.pointPolygonTest(c, (x, y), True) #pt = cv2.pointPolygonTest(c, (x, y), False) if pt > 3: result[x][y] = closing[x][y] return result.astype(np.float32)
def CloseInContour( mask, element ): large = 0 result = mask _, contours, _ = cv2.findContours(result,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #find the biggest area c = max(contours, key = cv2.contourArea) closing = cv2.morphologyEx(result, cv2.MORPH_CLOSE, element) for x in range(mask.shape[0]): for y in range(mask.shape[1]): #pt = cv2.pointPolygonTest(c, (x, y), True) pt = cv2.pointPolygonTest(c, (x, y), False) if pt == 1: result[x][y] = closing[x][y] return result.astype(np.float32)
def distToPolygon(contour, polygon): tests = [cv2.pointPolygonTest(polygon, (point[0][0], point[0][1]), True) for point in contour] return np.average(np.absolute(tests))
def find_color(self): contour = np.array(self.contour) # pdb.gimp_message(str(contour)) if len(contour) <= 1: return 0, 0, 0 # try with 9 directions dirx = [1, 1, 1, 0, 0, -1, -1, -1] diry = [1, 0, -1, 1, -1, 1, 0, -1] s_area = cv2.contourArea(contour, True) possible_colors = {} for point in contour: for dx, dy in zip(dirx, diry): new_cx, new_cy = int(point[0] + dx), int(point[1] + dy) # try it, try it # pdb.gimp_message("before polygon test") # dist = self.is_point_inside([new_cx, new_cy], index) dist = cv2.pointPolygonTest(contour, (new_cx, new_cy), True) # check the orientation of contour # pdb.gimp_message("after polygon test: " + str(dist)) if dist > 0: # pdb.gimp_message("Point " + str((new_cx, new_cx)) + " is inside") # voila # pdb.gimp_message("we have just to check the color") b, g, r = self.image[new_cy, new_cx] if (b, g, r) in possible_colors.keys(): possible_colors[(b, g, r)] += 1 else: possible_colors[(b, g, r)] = 1 # return self.image[new_cx, new_cy] max_occ, majority_color = 0, (0, 0, 0) for key, val in possible_colors.items(): # pdb.gimp_message("Color " + str(key) + " appears " + str(val)) if val > max_occ: max_occ, majority_color = val, key return majority_color[2], majority_color[1], majority_color[0]
def classify_monitor_contour_set(contours): '''Not a general purpose function : given the expectation of a set of strongly related contours for one monitor...''' # First pass : compute the center of mass of every contour classified = {} for (i,c) in enumerate(contours): classified[i] = {} classified[i]['contour'] = c moments = M = cv2.moments(c) classified[i]['com'] = (int(M['m10']/M['m00']), int(M['m01']/M['m00'])) rect = contour_to_monitor_coords(c) (maxWidth, maxHeight, dest, Mwarp) = compute_warp(rect) classified[i]['rect'] = rect classified[i]['maxWidth'] = maxWidth classified[i]['maxHeight'] = maxHeight classified[i]['dest'] = dest classified[i]['Mwarp'] = Mwarp # Second pass : establish if c-o-m of every contour is within the first contour reference_contour = contours[0] for (i,c) in enumerate(contours): classified[i]['coherent'] = cv2.pointPolygonTest(reference_contour, classified[i]['com'], False) # Final pass : report on the set print('$'*80) for (i,c) in enumerate(contours): print('%d : c-o-m %s : coherent : %d mw %d mh %d' % (i, classified[i]['com'], classified[i]['coherent'], classified[i]['maxWidth'], classified[i]['maxHeight'], )) print('$'*80) # From the contours coherent to the reference contour, build an average/best estimator count = 0 rect = np.zeros((4, 2), dtype = "float32") for (i,c) in enumerate(contours): if classified[i]['coherent'] == 1: count += 1 for j in range(0,4): rect[j] += classified[i]['rect'][j] #pdb.set_trace() for j in range(0,4): # BUG to show Alison # rect[j] = (rect[j]/1.0*count).astype('uint8') rect[j] = (rect[j]/(1.0*count)).astype('uint32') time.sleep(2.5) return rect
