K

app.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import gradio as gr
 
 
@@ -123,129 +122,3 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.launch(share = True)
-=======
-import gradio as gr
-
-
-
-import os
-import json
-from ultralytics import YOLO
-import supervision  as sv
-
-
-# --- 1. CONFIGURATION ---
-
-# Folder where the script is
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-
-# Go up one level to Om_Singh
-PROJECT_DIR = os.path.dirname(BASE_DIR)  # parent folder of app/
-
-# Paths for outputs in video_and_json folder
-VIDEO_DIR = os.path.join(PROJECT_DIR, "video_and_json")  # Om_Singh/video_and_json
-os.makedirs(VIDEO_DIR, exist_ok=True)  # ensure folder exists
-
-MODEL_PATH = os.path.join(BASE_DIR, "best.pt")  # model stays in app/
-OUTPUT_VIDEO_PATH = os.path.join(VIDEO_DIR, "output.mp4")
-OUTPUT_JSON_PATH  = os.path.join(VIDEO_DIR, "result.json")
-
-def process_video(INPUT_VIDEO_PATH, OUTPUT_VIDEO_PATH, OUTPUT_JSON_PATH):
-    print("Loading model...")
-    model = YOLO(MODEL_PATH)
-
-    print("Initializing tracker and annotators...")
-    tracker = sv.ByteTrack()
-    box_annotator = sv.BoxAnnotator(thickness=5)
-    label_annotator = sv.LabelAnnotator(text_position=sv.Position.TOP_CENTER, text_scale = 1, text_thickness= 1)
-
-    sv.LabelAnnotator()
-
-    frame_generator = sv.get_video_frames_generator(source_path=INPUT_VIDEO_PATH)
-    video_info = sv.VideoInfo.from_video_path(INPUT_VIDEO_PATH)
-
-    results_list = []
-
-    with sv.VideoSink(target_path=OUTPUT_VIDEO_PATH, video_info=video_info) as sink:
-        print("Processing video frames...")
-        for frame_number, frame in enumerate(frame_generator):
-            # Run YOLO prediction
-            results = model(frame)[0]
-            detections = sv.Detections.from_ultralytics(results)
-
-            # Update tracker
-            tracked_detections = tracker.update_with_detections(detections=detections)
-
-            # Prepare labels
-            labels = [
-                f"ID: {det[4]} {model.model.names[int(det[3])]}"
-                for det in tracked_detections
-            ]
-
-            # Annotate frame
-            annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=tracked_detections)
-            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=tracked_detections, labels=labels)
-
-            # Save tracking info
-            for det in tracked_detections:
-                bbox = det[0]
-                conf = det[2]
-                class_id = int(det[3])
-                tracker_id = det[4]
-
-                results_list.append({
-                    "frame_number": frame_number,
-                    "track_id": int(tracker_id),
-                    "class": model.model.names[class_id],
-                    "confidence": float(conf),
-                    "bounding_box": [int(coord) for coord in bbox]
-                })
-
-            # Write annotated frame
-            sink.write_frame(frame=annotated_frame)
-
-            if frame_number % 30 == 0:
-                print(f"Processed frame {frame_number}...")
-
-    print("Video processing complete. Saving results...")
-    with open(OUTPUT_JSON_PATH, 'w') as f:
-        json.dump(results_list, f, indent=4)
-
-    print("--- All tasks finished successfully! ---")
-
-
-
-# --- Main processing function ---
-def process(input_video):
-    output_video = "output.mp4"
-    output_json = "result.json"
-    
-    # During processing: red text
-    status_html = "<p style='color:red; font-weight:bold;'>Processing...</p>"
-    
-    # Run video processing
-    process_video(input_video, output_video, output_json)
-    
-    # After processing: green text
-    status_html = "<p style='color:limegreen; font-weight:bold;'>Processing complete!</p>"
-    return status_html, output_video, output_json
-
-# --- Gradio UI ---
-with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align:center;'>Vehicle and Pedestrian Tracker</h1>")
-
-    input_video = gr.Video(label="Upload Video")
-    start_btn = gr.Button("Start Tracking")
-    status_display = gr.HTML("")  # Initially empty
-    output_video = gr.Video(label="Processed Video")
-    output_json = gr.File(label="Download JSON Output")
-
-    start_btn.click(
-        fn=process,
-        inputs=input_video,
-        outputs=[status_display, output_video, output_json]
-    )
-
-if __name__ == "__main__":
-    demo.launch(share = True)
->>>>>>> e23ece8b8c3eb0b71e331ad00e70aa6c54f85b8b