# app.py

import torch
from transformers import AutoImageProcessor, AutoModelForObjectDetection
import cv2
from PIL import Image
import numpy as np
import gradio as gr
import os

# Import the Sort class from the local 'sort.py' file
# Pastikan file 'sort.py' ada di direktori yang sama dengan app.py
from sort import Sort

# --- LOAD MODELS AND TRACKER ONCE (PENTING!) ---
# Bagian ini hanya berjalan sekali saat aplikasi dimulai.
print("Loading model and processor...")
model_checkpoint = "facebook/detr-resnet-50"
image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
model = AutoModelForObjectDetection.from_pretrained(
    model_checkpoint,
    trust_remote_code=True
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
print("Model loaded successfully.")
# ---------------------------------------------------

def iou(boxA, boxB):
    # Fungsi untuk menghitung Intersection over Union (IoU)
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])
    interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
    iou_score = interArea / float(boxAArea + boxBArea - interArea)
    return iou_score

# --- FUNGSI PEMROSESAN UTAMA ---
def process_video(input_video_path):
    # Inisialisasi tracker dan penghitung untuk setiap video baru
    tracker = Sort(min_hits=3, iou_threshold=0.3)
    total_counts = {'person': 0, 'bicycle': 0, 'car': 0, 'motorcycle': 0}
    counted_ids = set()

    # Tentukan path output untuk video yang diproses
    output_video_path = "output.mp4"
    
    cap = cv2.VideoCapture(input_video_path)
    if not cap.isOpened():
        raise gr.Error(f"Could not open video file.")
    
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    
    # --- OPTIMISASI: Atur resolusi baru yang lebih kecil ---
    new_width = 960
    new_height = 540
    
    # Gunakan codec 'mp4v' yang kompatibel dan resolusi baru
    out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (new_width, new_height))
    
    frame_number = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        frame_number += 1
        
        # --- OPTIMISASI: Ubah ukuran setiap frame sebelum dideteksi ---
        frame = cv2.resize(frame, (new_width, new_height))

        # 1. Deteksi objek dengan DETR
        pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        inputs = image_processor(images=pil_image, return_tensors="pt").to(device)
        with torch.no_grad():
            outputs = model(**inputs)
        target_sizes = torch.tensor([pil_image.size[::-1]])
        results = image_processor.post_process_object_detection(outputs, threshold=0.6, target_sizes=target_sizes)[0]

        # 2. Format deteksi untuk SORT
        detections_for_sort = []
        original_detections = []
        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
            label_name = model.config.id2label[label.item()]
            if label_name in total_counts:
                box_list = box.tolist()
                detections_for_sort.append([box_list[0], box_list[1], box_list[2], box_list[3], score.item()])
                original_detections.append({'box': box_list, 'label': label_name})

        # 3. Update tracker
        tracked_objects_raw = []
        if len(detections_for_sort) > 0:
            tracked_objects_raw = tracker.update(np.array(detections_for_sort))

        # 4. Logika Penghitungan & Visualisasi
        for obj in tracked_objects_raw:
            x1, y1, x2, y2, obj_id = [int(val) for val in obj]
            
            best_iou = 0
            best_label = None
            for det in original_detections:
                iou_score = iou([x1, y1, x2, y2], det['box'])
                if iou_score > best_iou:
                    best_iou = iou_score
                    best_label = det['label']

            # Hitung objek jika ID-nya baru
            if best_label and obj_id not in counted_ids:
                total_counts[best_label] += 1
                counted_ids.add(obj_id)
            
            if best_label:
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
                cv2.putText(frame, f'{best_label} ID: {obj_id}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        
        # Tampilkan total hitungan kumulatif
        y_offset = 30
        for obj_name, count in total_counts.items():
            text = f'Total {obj_name.capitalize()}: {count}'
            cv2.putText(frame, text, (15, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 5)
            cv2.putText(frame, text, (15, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
            y_offset += 30
        
        out.write(frame)

    cap.release()
    out.release()
    
    print(f"Video processing finished. Total frames: {frame_number}")
    return output_video_path

# --- ANTARMUKA GRADIO (Dengan Layout Stabil) ---
with gr.Blocks(css="footer {visibility: hidden}") as demo:
    gr.Markdown("<h1>Real-Time Object Tracking & Counting with DETR and SORT</h1>")
    gr.Markdown("Upload a video to see object detection and tracking in action. This demo uses Facebook's DETR model for detection and the SORT algorithm to assign unique IDs and count objects.")

    with gr.Row():
        # Atur ukuran video yang tetap untuk mencegah layout "melompat"
        input_video = gr.Video(label="Input Video", width=640, height=360)
        output_video = gr.Video(label="Processed Video", width=640, height=360)

    submit_button = gr.Button("Submit", variant="primary")

    gr.Examples(
        examples=[['5402016-hd_1920_1080_30fps.mp4']],
        inputs=input_video,
        label="Click an example to run"
    )

    submit_button.click(
        fn=process_video,
        inputs=input_video,
        outputs=output_video
    )

# Jalankan aplikasi
demo.launch()