python大作业-基于OpenCV-Python和Mediapipe的俯卧撑计数应用系统源码（高分项目）.zip

from flask import Flask, render_template, Response, jsonify import cv2 import mediapipe as mp import numpy as np import PoseModule as pm app = Flask(__name__) @app.route('/') def index_1(): return render_template('index_1.html') @app.route('/index.html') def index(): return render_template('index.html') def calculate_body_balance(joints_data): # 在这里计算身体重心的位置 # 这里使用简单的平均值方法来计算重心 x = sum(j[0] for j in joints_data) / len(joints_data) y = sum(j[1] for j in joints_data) / len(joints_data) return x, y def calculate_movement_range(joints_data): # 在这里计算动作的幅度范围 # 这里使用简单的欧几里得距离来计算幅度 min_x = min(j[0] for j in joints_data) max_x = max(j[0] for j in joints_data) min_y = min(j[1] for j in joints_data) max_y = max(j[1] for j in joints_data) return ((max_x - min_x) ** 2 + (max_y - min_y) ** 2) ** 0.5 def check_pushup_range(lmList): # 计算动作的幅度范围 movement_range = calculate_movement_range(lmList) # 设置您的判断标准 min_range = 10 # 最小幅度范围 max_range = 1000 # 最大幅度范围 # 判断动作是否达标 if movement_range >= min_range and movement_range <= max_range: return True else: return False def calculate_center_of_gravity(lmList): # Extract landmark coordinates coords = [] for lm in lmList: x, y, _ = lm coords.append([x, y]) # Calculate centroid of the landmarks centroid = np.mean(coords, axis=0) # Calculate distance of markers from the centroid distances = [] for coord in coords: dist = np.linalg.norm(coord - centroid) distances.append(dist) # Calculate the weight of each marker weights = [float(dist / sum(distances)) for dist in distances] # 强制转换为float型 # Calculate center of gravity cog = np.array([0.0, 0.0]) for i, coord in enumerate(coords): coord = np.array(coord, dtype=float) # 转换为Numpy数组，并确保元素类型为float cog += weights[i] * coord return cog def generate_frames(): info = {"pushup_count": 0} cap = cv2.VideoCapture(0) detector = pm.poseDetector() count = 0 direction = 0 form = 0 progress_position = 0 feedback = "Fix Form" while cap.isOpened(): ret, img = cap.read() # 640 x 480 # Determine dimensions of video - Help with creation of box in Line 43 # print(width, height) img = detector.findPose(img, False) lmList = detector.findPosition(img, False) # print(lmList) if len(lmList) != 0: elbow = detector.findAngle(img, 11, 13, 15) shoulder = detector.findAngle(img, 13, 11, 23) hip = detector.findAngle(img, 11, 23, 25) # Percentage of success of pushup per = np.interp(elbow, (90, 160), (0, 100)) # Bar to show Pushup progress bar = np.interp(elbow, (90, 160), (380, 50)) # Check to ensure right form before starting the program if elbow > 160 and shoulder > 40 and hip > 160 and check_pushup_range(lmList): form = 1 # Check for full range of motion for the pushup if form == 1: if per == 0: if elbow <= 90 and hip > 160: feedback = "Up" if direction == 0: count += 0.5 direction = 1 else: feedback = "Fix Form" if per == 100: if elbow > 160 and shoulder > 40 and hip > 160: feedback = "Down" if direction == 1: count += 0.5 direction = 0 else: feedback = "Fix Form" # form = 0 # Draw Bar and Progress Bar if form == 1: cv2.rectangle(img, (580, 50), (600, 380), (255, 0, 0), 3) cv2.rectangle(img, (580, int(bar)), (600, 380), (255, 0, 0), cv2.FILLED) cv2.putText(img, f'{int(per)}%', (565, 430), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) # Add Progress Bar for Center of Gravity cog = calculate_center_of_gravity(lmList) x, y = cog balance = calculate_body_balance(lmList[11:25]) # 取得身体关键点的数据 distance_to_balance = np.linalg.norm(cog - np.array(balance)) # 计算距离 min_distance = 0 # 最小距离 max_distance = 50 # 最大距离 min_x = -5.0 # 缩放范围最小值 max_x = 5.0 # 缩放范围最大值 progress_position = int(np.interp(distance_to_balance, (min_distance, max_distance), (min_x, max_x))+5) if distance_to_balance <= 50: # 判断是否在推荐的重心范围内 balance_feedback = "In Balance" else: balance_feedback = "Out of Balance" progress_bar = "" for i in range(10): if i == progress_position: progress_bar += "|" else: progress_bar += "-" cv2.putText(img, 'CoG:', (20, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) cv2.putText(img, progress_bar, (100, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) cv2.putText(img, '({:.2f}, {:.2f})'.format(x, y), (220, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) cv2.putText(img, balance_feedback, (350, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2) # Pushup counter cv2.rectangle(img, (0, 380), (100, 480), (255, 255, 255), cv2.FILLED) cv2.putText(img, str(int(count)), (25, 455), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 5) # Feedback cv2.rectangle(img, (500, 0), (640, 40), (255, 255, 255), cv2.FILLED) cv2.putText(img, feedback, (500, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) info["pushup_count"] = count _, buffer = cv2.imencode('.jpg', img) frame = buffer.tobytes() yield frame, info cap.release() @app.route('/video_feed') def video_feed(): def inner(): for frame, _ in generate_frames(): yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') return Response(inner(), mimetype='multipart/x-mixed-replace; boundary=frame') @app.route('/data') def data(): _, info = next(generate_frames()) return jsonify(info) if __name__ == "__main__": app.run(debug=True)