Create app.py

app.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import gradio as gr
+
+# Initialize MediaPipe Pose and drawing utils.
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+mp_drawing = mp.solutions.drawing_utils
+
+# Calculate angle between three points.
+def calculate_angle(a, b, c):
+    a, b, c = np.array(a), np.array(b), np.array(c)
+    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
+    angle = np.abs(radians * 180.0 / np.pi)
+    return angle
+
+# Process the video and overlay lunge feedback.
+def analyze_lunges(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30
+
+    output_video = "output_lunges.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))
+
+    # Check lunge form based on leg angles.
+    def check_lunge_feedback(landmarks):
+        left_hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
+                    landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
+        left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
+                     landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
+        left_ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
+                      landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
+        right_hip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,
+                     landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]
+        right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
+                      landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]
+        right_ankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,
+                       landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]
+        left_leg_angle = calculate_angle(left_hip, left_knee, left_ankle)
+        right_leg_angle = calculate_angle(right_hip, right_knee, right_ankle)
+        left_accuracy = max(0, min(100, (1 - abs(left_leg_angle - 90) / 30) * 100))
+        right_accuracy = max(0, min(100, (1 - abs(right_leg_angle - 90) / 30) * 100))
+        feedback = "Correct Lunge"
+        if left_leg_angle < 70 or right_leg_angle < 70:
+            feedback = "Incorrect Lunge - Deep enough"
+        elif left_leg_angle > 110 or right_leg_angle > 110:
+            feedback = "Incorrect Lunge - Lower hips"
+        return feedback, left_accuracy, right_accuracy
+
+    # Draw separate accuracy bars for the left and right legs.
+    def draw_accuracy_bar(image, left_accuracy, right_accuracy):
+        bar_x, bar_y = 50, 400
+        bar_width, bar_height = 200, 20
+        left_fill_width = int((left_accuracy / 100) * bar_width)
+        cv2.rectangle(image, (bar_x, bar_y), (bar_x + bar_width, bar_y + bar_height), (200, 200, 200), 2)
+        cv2.rectangle(image, (bar_x, bar_y), (bar_x + left_fill_width, bar_y + bar_height), (0, 255, 0), -1)
+        cv2.putText(image, f"Left Leg Accuracy: {left_accuracy}%", (bar_x, bar_y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+        right_bar_y = bar_y + 50
+        right_fill_width = int((right_accuracy / 100) * bar_width)
+        cv2.rectangle(image, (bar_x, right_bar_y), (bar_x + bar_width, right_bar_y + bar_height), (200, 200, 200), 2)
+        cv2.rectangle(image, (bar_x, right_bar_y), (bar_x + right_fill_width, right_bar_y + bar_height), (0, 255, 0), -1)
+        cv2.putText(image, f"Right Leg Accuracy: {right_accuracy}%", (bar_x, right_bar_y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        # Process frame with MediaPipe.
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(image)
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        if results.pose_landmarks:
+            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+            feedback, left_accuracy, right_accuracy = check_lunge_feedback(results.pose_landmarks.landmark)
+            draw_accuracy_bar(image, left_accuracy, right_accuracy)
+            color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)
+            cv2.putText(image, feedback, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3)
+        out.write(image)
+    cap.release()
+    out.release()
+    return output_video
+
+# Gradio Interface for lunge analysis.
+gr.Interface(
+    fn=analyze_lunges,
+    inputs=gr.Video(),
+    outputs=gr.Video(),
+    title="Lunge Form Analyzer",
+    description="Upload a video of your lunges, and get feedback on your form!",
+).launch()