基于Python的手指尖检测

import cv2 import numpy as np # Open Camera object:打开摄像头 cap = cv2.VideoCapture(0) # Decrease frame size:设置摄像头的大小 cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) # do nothing def nothing(): pass # Start Main Program if __name__ == "__main__": while(True): # Capture frames from the camera ret, frame = cap.read() # Blur the image blur = cv2.blur(frame, (3, 3)) # Convert to HSV color space hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV) # Create a binary image with where white will be skin colors and rest is black mask2 = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Perform morphological transformations to filter out the background noise # Dilation increase skin color area # Erosion increase skin color area dilation = cv2.dilate(mask2, kernel_ellipse, iterations=1) erosion = cv2.erode(dilation, kernel_square, iterations=1) dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) filtered = cv2.medianBlur(dilation2, 5) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) median = cv2.medianBlur(dilation2, 5) ret2, thresh = cv2.threshold(median, 127, 255, 0) # Find contours of the filtered frame image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Draw Contours # cv2.drawContours(frame, contours, -1, (122, 122, 0), 3) # cv2.imshow('Dilation', median) if len(contours) > 0: # Find Max contour area (Assume that hand is in the frame) max_area = 10 ci = 0 for k in range(len(contours)): cnt = contours[k] area = cv2.contourArea(cnt) if area > max_area: max_area = area ci = k # Largest area contour cnts = contours[ci] # Find convex hull hull = cv2.convexHull(cnts) # Find convex defects hull2 = cv2.convexHull(cnts, returnPoints=False) defects = cv2.convexityDefects(cnts, hull2) # Get defect points and draw them in the original image FarDefect = [] for i in range(defects.shape[0]): s, e, f, d = defects[i, 0] start = tuple(cnts[s][0]) end = tuple(cnts[e][0]) far = tuple(cnts[f][0]) FarDefect.append(far) cv2.line(frame, start, end, [0, 255, 0], 1) cv2.circle(frame, far, 10, [100, 255, 255], 3) # Find moments of the largest contour moments = cv2.moments(cnts) # Central mass of first order moments if moments['m00'] != 0: cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 centerMass = (cx, cy) # Draw center mass cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(frame, 'Center', tuple(centerMass), font, 2, (255, 255, 255), 2) # Distance from each finger defect(finger webbing) to the center mass distanceBetweenDefectsToCenter = [] for i in range(0, len(FarDefect)): x = np.array(FarDefect[i]) centerMass = np.array(centerMass) distance = np.sqrt(np.power(x[0] - centerMass[0], 2) + np.power(x[1] - centerMass[1], 2)) distanceBetweenDefectsToCenter.append(distance) # Get an average of three shortest distances from finger webbing to center mass sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) # Get fingertip points from contour hull # If points are in proximity of 80 pixels, consider as a single point in the group finger = [] for i in range(0, len(hull) - 1): if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 80) or ( np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 80): if hull[i][0][1] < 500: finger.append(hull[i][0]) # The fingertip points are 5 hull points with largest y coordinates finger = sorted(finger, key=lambda x: x[1]) fingers = finger[0:5] # Calculate distance of each finger tip to the center mass fingerDistance = [] for i in range(0, len(fingers)): distance = np.sqrt( np.power(fingers[i][0] - centerMass[0], 2) + np.power(fingers[i][1] - centerMass[0], 2)) fingerDistance.append(distance) # Finger is pointed/raised if the distance of between fingertip to the center mass is larger # than the distance of average finger webbing to center mass by 130 pixels result = 0 for i in range(0, len(fingers)): if fingerDistance[i] > AverageDefectDistance + 130: result = result + 1 # Print number of pointed fingers cv2.putText(frame, str(result), (100, 100), font, 2, (255, 255, 255), 2) # Print bounding rectangle x, y, w, h = cv2.boundingRect(cnts) img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.drawContours(frame, [hull], -1, (255, 255, 255), 2) # Show final image cv2.imshow('Dilation', frame) # close the output video by pressing 'ESC' k = cv2.waitKey(5) & 0xFF if k == 27: break cap.release() cv2.destroyAllWindows()