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main.py

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+from flask import Flask, request, jsonify
+import os
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+import cv2
+
+app = Flask(__name__)
+
+# Define constants or parameters
+min_kick_angle = 30  # Minimum angle for the leg to be considered a kick
+frame_window = 10  # Number of frames to consider for action recognition
+kick_counter = 0
+highest_kick_frame = -1  # Initialize the frame number of the highest kick
+highest_kick_knee = None  # Initialize coordinates of the knee for the highest kick
+highest_kick_hip = None  # Initialize coordinates of the hip for the highest kick
+
+# Initialize variables for action recognition
+frame_buffer = []
+
+# Load the MoveNet model for pose estimation from TensorFlow Hub
+model = hub.load("https://tfhub.dev/google/movenet/singlepose/thunder/4")
+pose_net = model.signatures['serving_default']
+
+# Define upload folder for video files
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = {'mp4'}
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# Function to detect front kick based on keypoints
+def detect_front_kick_func(keypoints, frame_number):
+    keypoints_array = keypoints[0]  # Get the NumPy array from the list
+
+    right_hip = keypoints_array[0, 0, 8, :]  # Right hip is at index 8
+    right_knee = keypoints_array[0, 0, 9, :]  # Right knee is at index 9
+
+    # print(right_hip, ' ', right_knee)
+    if right_knee[2] < 0.4 and right_hip[2] < 0.4:
+        return False, -1, None, None
+
+    angle = np.arctan2(right_knee[1] - right_hip[1], right_knee[0] - right_hip[0]) * 180 / np.pi
+
+    if angle > min_kick_angle:
+        return True, frame_number, right_knee, right_hip
+    else:
+        return False, -1, None, None
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+@app.route('/detect_front_kick', methods=['POST'])
+def detect_front_kick():
+    try:
+        # Check if the 'video' field is in the request
+        if 'video' not in request.files:
+            return jsonify({'error': 'No video file provided'})
+
+        video_file = request.files['video']
+
+        # Check if the file has the allowed extension
+        if not allowed_file(video_file.filename):
+            return jsonify({'error': 'Invalid file format. Only MP4 videos are allowed.'})
+
+        # Save the video file to the upload folder with a secure name
+        video_filename = (video_file.filename)
+        video_filepath = os.path.join(app.config['UPLOAD_FOLDER'], video_filename)
+        video_file.save(video_filepath)
+
+        # Open the video file for processing
+        cap = cv2.VideoCapture(video_filepath)
+
+        # Check if the video file was opened successfully
+        if not cap.isOpened():
+            return jsonify({'error': 'Failed to open video file.'})
+
+        frame_number = 0  # Initialize frame number
+
+        while True:
+            ret, frame = cap.read()
+
+            if not ret:
+                break
+
+            # Preprocess the frame (resize, normalize, denoise, etc.)
+
+            # Perform pose estimation using MoveNet
+            resized_frame = cv2.resize(frame, (256, 256))
+            image = tf.constant(resized_frame, dtype=tf.int32)
+            image = tf.expand_dims(image, axis=0)
+
+            # Run model inference
+            outputs = pose_net(image)
+            keypoints = outputs['output_0'].numpy()
+
+            # Append the keypoints to the frame buffer
+            frame_buffer.append(keypoints)
+
+            # Maintain a sliding window of frames for action recognition
+            if len(frame_buffer) > frame_window:
+                frame_buffer.pop(0)
+
+            # Perform action recognition using the frame buffer
+            if len(frame_buffer) == frame_window:
+                is_kick, frame_with_kick, knee, hip = detect_front_kick_func(frame_buffer, frame_number)
+
+                if is_kick:
+                    kick_counter += 1
+                    if frame_with_kick > highest_kick_frame:
+                        highest_kick_frame = frame_with_kick
+                        highest_kick_knee = knee
+                        highest_kick_hip = hip
+
+            frame_number += 1
+
+        cap.release()
+
+        response_data = {
+            'kick_counter': kick_counter,
+            'highest_kick_frame': highest_kick_frame,
+            'highest_kick_knee': highest_kick_knee.tolist() if highest_kick_knee is not None else None,
+            'highest_kick_hip': highest_kick_hip.tolist() if highest_kick_hip is not None else None,
+        }
+
+        return jsonify(response_data)
+
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+@app.route('/home', methods=['GET'])
+def homie():
+    return jsonify({"message":"none"})
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+Flask==2.1.1
+numpy==1.21.2
+opencv-python==4.5.3.56
+tensorflow==2.5.1
+tensorflow-hub==0.12.0