ตัวอย่างการทำระบบ Hand Tracking ของมือ แบบ Real-time ด้วย MediaPipe Library ภาษา Python ของ Google สำหรับผู้ที่สนใจทำระบบการตรวจจับท่าทางของมือ
ในบทเรียนนี้อาจจะต้องศึกษาบทเรียนก่อนหน้า:
- OpenCV Archives | Game & Mobile Development AR VR XR (daydev.com)
- การประมวลผลรู้จำใบหน้า Face Recognition ด้วย Python
- Python การใช้ Deep Learning ปรับภาพขาวดำเป็นภาพสี BW to Color ร่วมกับ OpenCV
- Machine Learning ใช้ Python และ OpenCV ทำระบบ Measuring Size ขนาดของวัตถุในภาพ
เพียงแค่รอบนี้เราจะเตียม Python ของเราให้พร้อมครับ สร้างไฟล์ว่า Hand Tracking หลังจากนั้นให้เราไปใช้ตัวอย่าง Library บน GitHub ที่ชื่อว่า MediaPipe ที่นี่ครับ:
Home – mediapipe (google.github.io)
MediaPipe เป็น Machine Learning หรือ Deep Learning ตัวหนึ่งจาก Google ใช้ในการจับท่าทางของมือหรือ Hand Tracking ข้อดีคือความรวดเร็วแบบ Real-time และความง่ายในการเรียกใช้งานได้ อีกทั้ง MediaPipe นั้นก็ค่อนข้างแม่นยำอยู่ระดับหนึ่งเลยในการจับ มือของเรา
เพิ่มเติมอีกเล็กน้อย MediaPipe ยังมีอีกหลาย Solutions เทียบเท่า Face Recognition, Gesture Pose, Knife สำหรับ AR
ทำการติดตั้งโดยพิมพ์คำสั่ง:
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pip install mediapipe |
ทำการติดตั้ง MediaPipe เพื่อใช้งานร่วมกับ OpenCV
ทำการประกาศ OpenCV และจับ Capture Video จาก WebCam จาก Source ตัวอย่างได้เลย
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import copy import argparse import cv2 as cv import numpy as np import mediapipe as mp def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--device", type=int, default=0) parser.add_argument("--width", help='cap width', type=int, default=960) parser.add_argument("--height", help='cap height', type=int, default=540) parser.add_argument("--max_num_hands", type=int, default=2) parser.add_argument("--min_detection_confidence", help='min_detection_confidence', type=float, default=0.7) parser.add_argument("--min_tracking_confidence", help='min_tracking_confidence', type=int, default=0.5) parser.add_argument('--use_brect', action='store_true') args = parser.parse_args() return args def main(): # Parameter analysis ################################################################# args = get_args() cap_device = args.device cap_width = args.width cap_height = args.height max_num_hands = args.max_num_hands min_detection_confidence = args.min_detection_confidence min_tracking_confidence = args.min_tracking_confidence use_brect = args.use_brect # Camera preparation ############################################################### cap = cv.VideoCapture(cap_device) cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width) cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height) # Load Model ############################################################# mp_hands = mp.solutions.hands hands = mp_hands.Hands( max_num_hands=max_num_hands, min_detection_confidence=min_detection_confidence, min_tracking_confidence=min_tracking_confidence, ) while True: # Camera Capture ##################################################### ret, image = cap.read() if not ret: break image = cv.flip(image, 1) # ミラー表示 debug_image = copy.deepcopy(image) image = cv.cvtColor(image, cv.COLOR_BGR2RGB) results = hands.process(image) if results.multi_hand_landmarks is not None: for hand_landmarks, handedness in zip(results.multi_hand_landmarks, results.multi_handedness): cx, cy = calc_palm_moment(debug_image, hand_landmarks) brect = calc_bounding_rect(debug_image, hand_landmarks) debug_image = draw_landmarks(debug_image, cx, cy, hand_landmarks, handedness) debug_image = draw_bounding_rect(use_brect, debug_image, brect) cv.putText(debug_image, " ", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv.LINE_AA) key = cv.waitKey(1) if key == 27: # ESC break cv.imshow('MediaPipe Hand Tracking', debug_image) cap.release() cv.destroyAllWindows() def calc_palm_moment(image, landmarks): image_width, image_height = image.shape[1], image.shape[0] palm_array = np.empty((0, 2), int) for index, landmark in enumerate(landmarks.landmark): landmark_x = min(int(landmark.x * image_width), image_width - 1) landmark_y = min(int(landmark.y * image_height), image_height - 1) landmark_point = [np.array((landmark_x, landmark_y))] if index == 0: palm_array = np.append(palm_array, landmark_point, axis=0) if index == 1: palm_array = np.append(palm_array, landmark_point, axis=0) if index == 5: palm_array = np.append(palm_array, landmark_point, axis=0) if index == 9: palm_array = np.append(palm_array, landmark_point, axis=0) if index == 13: palm_array = np.append(palm_array, landmark_point, axis=0) if index == 17: palm_array = np.append(palm_array, landmark_point, axis=0) M = cv.moments(palm_array) cx, cy = 0, 0 if M['m00'] != 0: cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) return cx, cy def calc_bounding_rect(image, landmarks): image_width, image_height = image.shape[1], image.shape[0] landmark_array = np.empty((0, 2), int) for _, landmark in enumerate(landmarks.landmark): landmark_x = min(int(landmark.x * image_width), image_width - 1) landmark_y = min(int(landmark.y * image_height), image_height - 1) landmark_point = [np.array((landmark_x, landmark_y))] landmark_array = np.append(landmark_array, landmark_point, axis=0) x, y, w, h = cv.boundingRect(landmark_array) return [x, y, x + w, y + h] def draw_landmarks(image, cx, cy, landmarks, handedness): image_width, image_height = image.shape[1], image.shape[0] landmark_point = [] for index, landmark in enumerate(landmarks.landmark): if landmark.visibility < 0 or landmark.presence < 0: continue landmark_x = min(int(landmark.x * image_width), image_width - 1) landmark_y = min(int(landmark.y * image_height), image_height - 1) # landmark_z = landmark.z landmark_point.append((landmark_x, landmark_y)) if index == 0: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 1: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 2: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 3: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 4: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 5: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 6: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 7: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 8: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 9: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 10: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 11: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 12: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 13: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 14: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 15: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 16: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 17: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 18: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 19: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 20: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if len(landmark_point) > 0: cv.line(image, landmark_point[2], landmark_point[3], (0, 255, 0), 2) cv.line(image, landmark_point[3], landmark_point[4], (0, 255, 0), 2) cv.line(image, landmark_point[5], landmark_point[6], (0, 255, 0), 2) cv.line(image, landmark_point[6], landmark_point[7], (0, 255, 0), 2) cv.line(image, landmark_point[7], landmark_point[8], (0, 255, 0), 2) cv.line(image, landmark_point[9], landmark_point[10], (0, 255, 0), 2) cv.line(image, landmark_point[10], landmark_point[11], (0, 255, 0), 2) cv.line(image, landmark_point[11], landmark_point[12], (0, 255, 0), 2) cv.line(image, landmark_point[13], landmark_point[14], (0, 255, 0), 2) cv.line(image, landmark_point[14], landmark_point[15], (0, 255, 0), 2) cv.line(image, landmark_point[15], landmark_point[16], (0, 255, 0), 2) cv.line(image, landmark_point[17], landmark_point[18], (0, 255, 0), 2) cv.line(image, landmark_point[18], landmark_point[19], (0, 255, 0), 2) cv.line(image, landmark_point[19], landmark_point[20], (0, 255, 0), 2) cv.line(image, landmark_point[0], landmark_point[1], (0, 255, 0), 2) cv.line(image, landmark_point[1], landmark_point[2], (0, 255, 0), 2) cv.line(image, landmark_point[2], landmark_point[5], (0, 255, 0), 2) cv.line(image, landmark_point[5], landmark_point[9], (0, 255, 0), 2) cv.line(image, landmark_point[9], landmark_point[13], (0, 255, 0), 2) cv.line(image, landmark_point[13], landmark_point[17], (0, 255, 0), 2) cv.line(image, landmark_point[17], landmark_point[0], (0, 255, 0), 2) if len(landmark_point) > 0: cv.circle(image, (cx, cy), 12, (0, 255, 0), 2) cv.putText(image, handedness.classification[0].label[0], (cx - 6, cy + 6), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv.LINE_AA) return image def draw_bounding_rect(use_brect, image, brect): if use_brect: cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]), (0, 255, 0), 2) return image if __name__ == '__main__': main() |
Code ดูยาวอธิบายง่ายๆ
จะใช้การจับภาพจาก webcam แบบ Real-Time ต่อเนื่องโดยใช้ while loop แสดงผลต่อกันเป็นวิดีโอ
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while True: # Camera Capture ##################################################### ret, image = cap.read() if not ret: break image = cv.flip(image, 1) # ミラー表示 debug_image = copy.deepcopy(image) image = cv.cvtColor(image, cv.COLOR_BGR2RGB) results = hands.process(image) if results.multi_hand_landmarks is not None: for hand_landmarks, handedness in zip(results.multi_hand_landmarks, results.multi_handedness): cx, cy = calc_palm_moment(debug_image, hand_landmarks) brect = calc_bounding_rect(debug_image, hand_landmarks) debug_image = draw_landmarks(debug_image, cx, cy, hand_landmarks, handedness) debug_image = draw_bounding_rect(use_brect, debug_image, brect) cv.putText(debug_image, " ", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv.LINE_AA) key = cv.waitKey(1) if key == 27: # ESC break cv.imshow('MediaPipe Hand Tracking', debug_image) cap.release() cv.destroyAllWindows() |
จับ Palm connector มือของเราทันที
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def draw_landmarks(image, cx, cy, landmarks, handedness): image_width, image_height = image.shape[1], image.shape[0] landmark_point = [] for index, landmark in enumerate(landmarks.landmark): if landmark.visibility < 0 or landmark.presence < 0: continue landmark_x = min(int(landmark.x * image_width), image_width - 1) landmark_y = min(int(landmark.y * image_height), image_height - 1) # landmark_z = landmark.z landmark_point.append((landmark_x, landmark_y)) if index == 0: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 1: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 2: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 3: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 4: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 5: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 6: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 7: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 8: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 9: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 10: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 11: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 12: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 13: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 14: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 15: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 16: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if index == 17: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 18: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 19: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) if index == 20: cv.circle(image, (landmark_x, landmark_y), 5, (0, 255, 0), 2) cv.circle(image, (landmark_x, landmark_y), 12, (0, 255, 0), 2) if len(landmark_point) > 0: cv.line(image, landmark_point[2], landmark_point[3], (0, 255, 0), 2) cv.line(image, landmark_point[3], landmark_point[4], (0, 255, 0), 2) cv.line(image, landmark_point[5], landmark_point[6], (0, 255, 0), 2) cv.line(image, landmark_point[6], landmark_point[7], (0, 255, 0), 2) cv.line(image, landmark_point[7], landmark_point[8], (0, 255, 0), 2) cv.line(image, landmark_point[9], landmark_point[10], (0, 255, 0), 2) cv.line(image, landmark_point[10], landmark_point[11], (0, 255, 0), 2) cv.line(image, landmark_point[11], landmark_point[12], (0, 255, 0), 2) cv.line(image, landmark_point[13], landmark_point[14], (0, 255, 0), 2) cv.line(image, landmark_point[14], landmark_point[15], (0, 255, 0), 2) cv.line(image, landmark_point[15], landmark_point[16], (0, 255, 0), 2) cv.line(image, landmark_point[17], landmark_point[18], (0, 255, 0), 2) cv.line(image, landmark_point[18], landmark_point[19], (0, 255, 0), 2) cv.line(image, landmark_point[19], landmark_point[20], (0, 255, 0), 2) cv.line(image, landmark_point[0], landmark_point[1], (0, 255, 0), 2) cv.line(image, landmark_point[1], landmark_point[2], (0, 255, 0), 2) cv.line(image, landmark_point[2], landmark_point[5], (0, 255, 0), 2) cv.line(image, landmark_point[5], landmark_point[9], (0, 255, 0), 2) cv.line(image, landmark_point[9], landmark_point[13], (0, 255, 0), 2) cv.line(image, landmark_point[13], landmark_point[17], (0, 255, 0), 2) cv.line(image, landmark_point[17], landmark_point[0], (0, 255, 0), 2) if len(landmark_point) > 0: cv.circle(image, (cx, cy), 12, (0, 255, 0), 2) cv.putText(image, handedness.classification[0].label[0], (cx - 6, cy + 6), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv.LINE_AA) return image |
ค่อนข้างมีการประมวลผลเยอะ มันจะวิ่งหาตำแหน่ง Landmark บนมือของเราระบุรายละเอียด ขนาดที่แตกต่างกันของมือแต่ละข้าง เก็บข้อมูลที่ได้มาจาก LandMark ไว้ในตัวแปร หากสังเกตจะเห็นว่ามีหลาย landmark มาก
เอาละเราลองมา Track และแสดงผลท่าทางของมือกันหน่อยมี Video นะ
บทเรียนหน้าเป็น Holistic จับ Body และ ใบหน้า Source ก็ใน Github นั่นแหละครับ เลยรีวิวไม่เยอะมาก Copy ก็ใช้ได้แล้ว!
