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import gradio as gr |
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from ultralytics import YOLO |
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import cv2 |
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import numpy as np |
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import tempfile |
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import os |
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import shutil |
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model = YOLO("best.pt") |
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bagages_classes = ['suitcase', 'backpack', 'handbag'] |
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def detect_objects_image(image): |
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results = model(image)[0] |
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annotated_image = image.copy() |
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count_by_class = {cls: 0 for cls in bagages_classes} |
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colors = { |
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'suitcase': (0, 255, 0), |
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'backpack': (255, 0, 0), |
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'handbag': (0, 128, 255) |
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} |
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for box in results.boxes: |
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class_id = int(box.cls) |
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class_name = model.names[class_id] |
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if class_name in bagages_classes: |
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x1, y1, x2, y2 = map(int, box.xyxy[0]) |
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conf = float(box.conf[0]) |
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color = colors.get(class_name, (255, 255, 255)) |
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label = f"{class_name} ({conf:.2f})" |
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2) |
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cv2.rectangle(annotated_image, (x1, y1), (x2, y2), color, 2) |
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cv2.rectangle(annotated_image, (x1, y1 - th - 10), (x1 + tw + 4, y1), color, -1) |
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cv2.putText(annotated_image, label, (x1 + 2, y1 - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2) |
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count_by_class[class_name] += 1 |
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resume = "📷 Résumé détection image:\n" |
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total = sum(count_by_class.values()) |
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resume += f"Total bagages détectés : {total}\n" |
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for cls, count in count_by_class.items(): |
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resume += f"🧳{cls}: {count}\n" |
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return annotated_image, resume |
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def detect_objects_video(video): |
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import time |
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import cv2 |
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import os |
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import shutil |
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import tempfile |
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video_path = video if isinstance(video, str) else video.name |
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cap = cv2.VideoCapture(video_path) |
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if not cap.isOpened(): |
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print("Impossible d'ouvrir la vidéo.") |
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return None |
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fps = cap.get(cv2.CAP_PROP_FPS) |
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W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
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H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
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midline = W // 2 |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp: |
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temp_output_path = tmp.name |
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out = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (W, H)) |
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counted_ids_direction = set() |
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count_by_class = {cls: 0 for cls in bagages_classes} |
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previous_positions = {} |
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entrants = sortants = 0 |
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class_colors = { |
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'suitcase': (0, 255, 0), |
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'backpack': (255, 0, 0), |
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'handbag': (0, 0, 255) |
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} |
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results = model.track(source=video_path, persist=True, stream=True, tracker="botsort.yaml", device='cpu') |
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for result in results: |
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frame = getattr(result, 'orig_img', None) |
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boxes = getattr(result, 'boxes', None) |
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if frame is None: |
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continue |
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cv2.line(frame, (midline, 0), (midline, H), (0, 0, 255), 2) |
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if boxes is not None and boxes.id is not None: |
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ids = boxes.id.cpu().numpy().astype(int) |
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clss = boxes.cls.cpu().numpy().astype(int) |
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confs = boxes.conf.cpu().numpy() |
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coords = boxes.xyxy.cpu().numpy() |
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for obj_id, cls_id, box, conf in zip(ids, clss, coords, confs): |
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class_name = model.names[int(cls_id)] |
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if class_name not in bagages_classes: |
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continue |
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x1, y1, x2, y2 = map(int, box) |
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cx = int((x1 + x2) / 2) |
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prev_cx = previous_positions.get(obj_id, cx) |
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direction = None |
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if prev_cx < midline <= cx: |
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direction = 'in' |
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elif prev_cx > midline >= cx: |
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direction = 'out' |
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previous_positions[obj_id] = cx |
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if direction: |
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key = (obj_id, direction) |
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if key not in counted_ids_direction: |
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counted_ids_direction.add(key) |
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if direction == 'in': |
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entrants += 1 |
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else: |
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sortants += 1 |
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count_by_class[class_name] += 1 |
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color = class_colors.get(class_name, (255, 255, 255)) |
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label = f"{class_name} #{obj_id} {conf*100:.1f}%" |
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) |
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) |
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cv2.rectangle(frame, (x1, y1 - 20), (x1 + tw + 6, y1), color, -1) |
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cv2.putText(frame, label, (x1 + 3, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) |
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total = len(counted_ids_direction) |
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line1 = f"Total: {total} Entrants: {entrants} Sortants: {sortants}" |
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line2 = " ".join([f"{cls}: {count_by_class[cls]}" for cls in bagages_classes]) |
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(w1, h1), _ = cv2.getTextSize(line1, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2) |
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(w2, h2), _ = cv2.getTextSize(line2, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2) |
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pad_x, pad_y = 10, 10 |
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text_height = h1 + h2 + pad_y * 3 |
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text_width = max(w1, w2) + pad_x * 2 |
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top_left = (10, 10) |
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bottom_right = (top_left[0] + text_width, top_left[1] + text_height) |
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cv2.rectangle(frame, top_left, bottom_right, (50, 50, 50), -1) |
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org1 = (top_left[0] + pad_x, top_left[1] + h1 + pad_y) |
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cv2.putText(frame, line1, org1, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 3) |
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cv2.putText(frame, line1, org1, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1) |
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org2 = (top_left[0] + pad_x, org1[1] + h2 + pad_y) |
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cv2.putText(frame, line2, org2, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 3) |
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cv2.putText(frame, line2, org2, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1) |
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out.write(frame) |
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cap.release() |
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out.release() |
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os.makedirs("videos_sortie", exist_ok=True) |
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final_output_path = os.path.join("videos_sortie", "bagages_detectes.mp4") |
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shutil.copy(temp_output_path, final_output_path) |
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os.remove(temp_output_path) |
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resume = f"🎥 Résumé vidéo :\n" |
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resume += f"🎒 Total bagages détectés (franchissement ligne rouge): {len(counted_ids_direction)}\n" |
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for cls in bagages_classes: |
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resume += f"🧳 {cls}: {count_by_class[cls]}\n" |
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resume += f"\n✅ Entrants: {entrants}\n🚪 Sortants: {sortants}" |
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return final_output_path, resume, final_output_path |
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with gr.Blocks(theme="SebastianBravo/simci_css") as demo: |
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gr.Markdown( |
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""" |
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<h1 style="text-align: center;">🎯 Détection et traçage de bagages</h1> |
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<p style="text-align: center;">Détecte et compte automatiquement les bagages avec YOLOv8 : <strong>suitcase</strong>, <strong>backpack</strong>, <strong>handbag</strong>.</p> |
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<p style="text-align: center;">Téléverse une image ou une vidéo, et laisse l'IA faire le travail 🧠</p> |
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""" |
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) |
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with gr.Tab("🖼️Détection sur Image"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_input = gr.Image(type="numpy", label="📥 Charger une image") |
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image_btn = gr.Button("🔍 Lancer la détection") |
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gr.Examples( |
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examples=[ |
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"Image1.jpg", |
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"Image2.jpg", |
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"Image3.jpg" |
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], |
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inputs=image_input, |
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label="📂 Exemples d’images" |
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) |
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with gr.Column(scale=1): |
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image_output = gr.Image(type="numpy", label="📸 Image détectée") |
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image_summary = gr.Textbox(label="📋 Résumé", lines=4, interactive=False) |
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image_btn.click(fn=detect_objects_image, inputs=image_input, outputs=[image_output, image_summary]) |
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with gr.Tab("🎞️ Détection sur Vidéo"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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video_input = gr.Video(label="📥 Charger une vidéo (.mp4)") |
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video_btn = gr.Button("🔎 Lancer la détection vidéo") |
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with gr.Column(scale=1): |
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video_output = gr.Video(label="🎬 Vidéo traitée") |
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video_info = gr.Textbox(label="📊 Résumé", lines=6, interactive=False) |
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video_download = gr.File(label="⬇️ Télécharger la vidéo") |
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def run_video_pipeline(video): |
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output_path, resume, file_path = detect_objects_video(video) |
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return output_path, resume, file_path |
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video_btn.click(fn=run_video_pipeline, inputs=video_input, outputs=[video_output, video_info, video_download]) |
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with gr.Accordion("ℹ️ Astuce", open=False): |
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gr.Markdown(""" |
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**🔎 Bagages détectés :** |
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- Valise (`suitcase`) |
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- Sac à dos (`backpack`) |
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- Sac à main (`handbag`) |
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**Fonctionnalités de l'application :** |
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✅ Suivi des bagages en temps réel |
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✅ Comptage unique après franchissement de la ligne rouge |
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✅ Résumé en texte + vidéo annotée |
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✅ Téléchargement de la vidéo traitée |
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**Cas d'usage sécurité :** |
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🧳 Surveillance de bagages : |
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- Compter les bagages entrants/sortants |
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- Détection d’abandon ou de dépôt suspect |
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🚫 Zones sensibles : |
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- Détection de franchissement interdit |
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- Alerte en cas d’objet abandonné ou non autorisé |
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""") |
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demo.launch() |
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