Add app files

app.py

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+import cv2
+import gradio as gr
+from ultralytics import YOLO
+import numpy as np
+import math
+import tempfile
+import os
+
+# Load the model
+# yolov8n-pose.pt will be automatically downloaded if not present
+model = YOLO('yolov8n-pose.pt')
+
+def get_angle(p1, p2, p3):
+    """
+    Calculate the angle between three points (p1-p2-p3) at joint p2.
+    """
+    rad = math.atan2(p3[1]-p2[1], p3[0]-p2[0]) - math.atan2(p1[1]-p2[1], p1[0]-p2[0])
+    deg = abs(rad * 180.0 / math.pi)
+    return 360 - deg if deg > 180 else deg
+
+def draw_pose(img, kps):
+    """
+    Draw pose landmarks and classify posture based on knee angle.
+    """
+    legs = [(11, 13, 15), (12, 14, 16)] # hip-knee-ankle indices
+    status = "Unknown"
+
+    for h_idx, k_idx, a_idx in legs:
+        hip, knee, ankle = kps[h_idx], kps[k_idx], kps[a_idx]
+        
+        # Check confidence scores (index 2)
+        if hip[2] > 0.5 and knee[2] > 0.5 and ankle[2] > 0.5:
+            ang = get_angle(hip[:2], knee[:2], ankle[:2])
+            
+            if ang > 160: 
+                posture, color = "STANDING", (0, 255, 0)
+            elif ang < 140: 
+                posture, color = "SITTING", (255, 0, 0)
+            else: 
+                posture, color = "BENDING", (0, 165, 255)
+            
+            # Draw lines and text
+            pt = lambda p: (int(p[0]), int(p[1]))
+            cv2.line(img, pt(hip), pt(knee), (255,0,255), 2)
+            cv2.line(img, pt(knee), pt(ankle), (255,0,255), 2)
+            
+            label = f"{posture} {int(ang)}"
+            (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
+            cv2.rectangle(img, (int(hip[0]), int(hip[1]) - 30), (int(hip[0]) + w, int(hip[1])), color, -1)
+            cv2.putText(img, label, (int(hip[0]), int(hip[1]) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+            
+            return posture
+            
+    return status
+
+def process_img(img):
+    """
+    Process a single image for posture detection.
+    """
+    if img is None: 
+        return None, "No Image"
+    
+    # Run inference
+    res = model(img)
+    out = img.copy()
+    msg = "No Person Detected"
+    
+    if res and res[0].keypoints is not None:
+        for k in res[0].keypoints.data.cpu().numpy():
+             msg = f"POSTURE: {draw_pose(out, k)}"
+             
+    return out, msg
+
+def process_vid(vid_path):
+    """
+    Process a video file for posture detection.
+    """
+    if not vid_path: 
+        return None
+        
+    cap = cv2.VideoCapture(vid_path)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    # Create temp file for output
+    fd, out_path = tempfile.mkstemp(suffix='.mp4')
+    os.close(fd)
+    
+    # Initialize video writer
+    writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret: 
+            break
+        
+        # Run inference
+        res = model(frame, verbose=False)
+        if res and res[0].keypoints is not None:
+            for k in res[0].keypoints.data.cpu().numpy():
+                draw_pose(frame, k)
+                
+        writer.write(frame)
+        
+    cap.release()
+    writer.release()
+    return out_path
+
+# Define Gradio Interface
+with gr.Blocks(title="Postures") as app:
+    gr.Markdown("# Posture Detection\nSimple angle-based classification using YOLOv8: Standing (>160), Sitting (<140)")
+    
+    with gr.Tab("Image"):
+        with gr.Row():
+            with gr.Column():
+                img_inp = gr.Image(type="numpy", label="Input Image")
+                img_btn = gr.Button("Detect Posture")
+            with gr.Column():
+                img_out = gr.Image(label="Result")
+                img_stat = gr.Textbox(label="Status")
+        img_btn.click(process_img, inputs=img_inp, outputs=[img_out, img_stat])
+        
+    with gr.Tab("Video"):
+        with gr.Row():
+            with gr.Column():
+                vid_inp = gr.Video(label="Input Video")
+                vid_btn = gr.Button("Process Video")
+            with gr.Column():
+                vid_out = gr.Video(label="Processed Video")
+        vid_btn.click(process_vid, inputs=vid_inp, outputs=vid_out)
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+ultralytics
+gradio
+opencv-python
+numpy