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backend.py

@@ -1,66 +0,0 @@
-import os
-import json
-import tempfile
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import FileResponse
-from ultralytics import YOLO
-import supervision as sv
-
-# --- MODEL AND APP INITIALIZATION ---
-# The model is loaded only ONCE when the server starts, making it fast.
-MODEL_PATH = "best.pt"
-model = YOLO(MODEL_PATH)
-
-app = FastAPI(title="YOLOv8 Tracking API")
-
-# --- YOUR EXISTING PROCESSING LOGIC (UNCHANGED) ---
-# We just put your code inside this function.
-def process_video_logic(input_path, output_path, json_path):
-    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)
-    frame_generator = sv.get_video_frames_generator(source_path=input_path)
-    video_info = sv.VideoInfo.from_video_path(input_path)
-    results_list = []
-
-    with sv.VideoSink(target_path=output_path, video_info=video_info) as sink:
-        for frame_number, frame in enumerate(frame_generator):
-            results = model(frame, verbose=False)[0]
-            detections = sv.Detections.from_ultralytics(results)
-            tracked_detections = tracker.update_with_detections(detections=detections)
-            labels = [f"ID: {det[4]} {model.model.names[int(det[3])]}" for det in tracked_detections]
-            annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=tracked_detections)
-            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=tracked_detections, labels=labels)
-            
-            for det in tracked_detections:
-                bbox, conf, class_id, tracker_id = det[0], det[2], int(det[3]), 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]
-                })
-            sink.write_frame(frame=annotated_frame)
-
-    with open(json_path, 'w') as f:
-        json.dump(results_list, f, indent=4)
-
-# --- API ENDPOINT ---
-@app.post("/track/")
-async def track_video_endpoint(video: UploadFile = File(...)):
-    # Use a temporary directory to handle file operations safely
-    with tempfile.TemporaryDirectory() as temp_dir:
-        input_path = os.path.join(temp_dir, video.filename)
-        output_video_path = os.path.join(temp_dir, "output.mp4")
-        output_json_path = os.path.join(temp_dir, "results.json")
-
-        # Save the uploaded video file
-        with open(input_path, "wb") as buffer:
-            buffer.write(await video.read())
-
-        # Run your existing processing logic
-        process_video_logic(input_path, output_video_path, output_json_path)
-
-        # Return the processed video as a downloadable file
-        return FileResponse(output_video_path, media_type="video/mp4", filename="output.mp4")