Create app.py

app.py

@@ -0,0 +1,122 @@
+import os
+import cv2
+import mediapipe as mp
+import numpy as np
+import tempfile
+import warnings
+import gradio as gr
+
+# --- Silence unnecessary logs and warnings ---
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ['GLOG_minloglevel'] = '2'
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", message="SymbolDatabase.GetPrototype")
+
+# --- Main processing function ---
+def process_pose(video_path, mode, min_det_conf, min_track_conf, progress=gr.Progress()):
+    try:
+        progress(0, desc="Initializing pose model...")
+        mp_drawing = mp.solutions.drawing_utils
+        mp_pose = mp.solutions.pose
+
+        # Temporary output file
+        temp_dir = tempfile.mkdtemp()
+        output_path = os.path.join(temp_dir, "output.mp4")
+
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+
+        if fps == 0 or total_frames == 0:
+            raise ValueError("Could not read the video or it is empty.")
+
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+        with mp_pose.Pose(
+            min_detection_confidence=min_det_conf,
+            min_tracking_confidence=min_track_conf
+        ) as pose:
+
+            frame_idx = 0
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                frame_idx += 1
+                progress(frame_idx / total_frames, desc=f"Processing frame {frame_idx}/{total_frames}")
+
+                image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                results = pose.process(image)
+                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+                if mode == "Pose on original video":
+                    output_frame = image.copy()
+                    mp_drawing.draw_landmarks(
+                        output_frame,
+                        results.pose_landmarks,
+                        mp_pose.POSE_CONNECTIONS,
+                        mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
+                        mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
+                    )
+
+                else:  # Pose only (black background)
+                    mask = np.zeros_like(image)
+                    mp_drawing.draw_landmarks(
+                        mask,
+                        results.pose_landmarks,
+                        mp_pose.POSE_CONNECTIONS,
+                        mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
+                        mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
+                    )
+                    output_frame = cv2.addWeighted(np.zeros_like(image), 1, mask, 0.8, 0)
+
+                out.write(output_frame)
+
+        cap.release()
+        out.release()
+        progress(1, desc="Completed ✅")
+        return output_path
+
+    except Exception as e:
+        return f"❌ Error during processing: {e}"
+
+# --- Custom HTML for warnings and explanations ---
+warning_html = """
+<div style="text-align:center; color:red; font-weight:bold;">
+⚠️ Reminder: Video must be under 5 MB due to CPU processing time.<br>
+⚠️ Processing long videos may take a considerable amount of time.
+</div>
+"""
+
+param_info_html = """
+<div style="border: 2px solid #4CAF50; padding: 10px; border-radius: 8px; margin:10px; background-color:#f9f9f9;">
+<b>Parameters:</b><br>
+- <b>min_detection_confidence:</b> Minimum confidence for the model to detect a pose.<br>
+- <b>min_tracking_confidence:</b> Minimum confidence for the model to track the pose across frames.
+</div>
+"""
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=process_pose,
+    inputs=[
+        gr.Video(label="🎥 Upload your video", sources=["upload"], elem_id="video_upload"),
+        gr.Radio(
+            ["Pose on original video", "Pose only (black background)"],
+            label="Output mode",
+            value="Pose on original video"
+        ),
+        gr.Slider(0.0, 1.0, value=0.5, label="min_detection_confidence"),
+        gr.Slider(0.0, 1.0, value=0.5, label="min_tracking_confidence"),
+    ],
+    outputs=gr.Video(label="📦 Processed Video"),
+    title="<center>Pose Estimation - MediaPipe (CPU Optimized)</center>",
+    description=warning_html + param_info_html,
+)
+
+if __name__ == "__main__":
+    iface.launch()