Delete app.py

app.py

@@ -1,427 +0,0 @@
-import os
-import tempfile
-import cv2
-import numpy as np
-import gradio as gr
-import supervision as sv
-from ultralytics import YOLO
-
-DEFAULT_MAX_FRAME_SIZE = 640
-DEFAULT_DETECT_EVERY_N_FRAMES = 2
-DEFAULT_ZONE_MARGIN = 0.10
-
-model = YOLO("yolov8n.pt")
-CLASS_NAMES_DICT = model.model.names
-
-SELECTED_CLASS_NAMES = ['car', 'truck', 'bus', 'motorcycle', ]
-SELECTED_CLASS_IDS = [
-    {value: key for key, value in CLASS_NAMES_DICT.items()}[name]
-    for name in SELECTED_CLASS_NAMES
-]
-
-box_annotator = sv.BoxAnnotator(thickness=4)
-label_annotator = sv.LabelAnnotator(text_thickness=2, text_scale=1.5, text_color=sv.Color.BLACK)
-trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
-def process_video(
-    video_path,
-    use_resize: bool = True,
-    max_frame_size: int = DEFAULT_MAX_FRAME_SIZE,
-    detect_every_n: int = DEFAULT_DETECT_EVERY_N_FRAMES,
-    line_orientation: str = "Ngang",
-    zone_margin: float = DEFAULT_ZONE_MARGIN,
-):
-    if video_path is None:
-        return None
-
-    if isinstance(video_path, dict):
-        video_path = video_path.get("path", video_path)
-
-    video_info = sv.VideoInfo.from_video_path(video_path)
-
-    byte_tracker = sv.ByteTrack(
-        track_activation_threshold=0.25,
-        lost_track_buffer=30,
-        minimum_matching_threshold=0.8,
-        frame_rate=video_info.fps or 30,
-        minimum_consecutive_frames=3
-    )
-    byte_tracker.reset()
-
-    class_counts = {name: 0 for name in SELECTED_CLASS_NAMES}
-    counted_ids = set()
-
-    def callback(frame: np.ndarray, index: int) -> np.ndarray:
-        nonlocal class_counts, counted_ids
-
-        if max_frame_size is None or max_frame_size <= 0:
-            max_size = DEFAULT_MAX_FRAME_SIZE
-        else:
-            max_size = int(max_frame_size)
-
-        if detect_every_n is None or detect_every_n < 1:
-            detect_every = 1
-        else:
-            detect_every = int(detect_every_n)
-
-        fh_orig, fw_orig = frame.shape[:2]
-        if use_resize:
-            scale = min(1.0, max_size / max(fh_orig, fw_orig))
-            if scale < 1.0:
-                frame_infer = cv2.resize(
-                    frame, (int(fw_orig * scale), int(fh_orig * scale))
-                )
-            else:
-                frame_infer = frame
-        else:
-            frame_infer = frame
-
-        fh, fw = frame_infer.shape[:2]
-        if line_orientation == "Dọc":
-            line_pos = int(fw * 0.5)
-            is_horizontal = False
-        else:
-            line_pos = int(fh * 0.5)
-            is_horizontal = True
-
-        if zone_margin is None or zone_margin <= 0:
-            zm_ratio = DEFAULT_ZONE_MARGIN
-        else:
-            zm_ratio = max(0.01, min(0.5, float(zone_margin)))
-
-        if is_horizontal:
-            z_half = int(fh * zm_ratio)
-            z_top = max(0, line_pos - z_half)
-            z_bot = min(fh - 1, line_pos + z_half)
-        else:
-            z_half = int(fw * zm_ratio)
-            z_left = max(0, line_pos - z_half)
-            z_right = min(fw - 1, line_pos + z_half)
-
-        if detect_every > 1 and index % detect_every != 0:
-            annotator_frame = frame_infer.copy()
-            overlay_zone = annotator_frame.copy()
-            if is_horizontal:
-                cv2.rectangle(
-                    overlay_zone,
-                    (0, z_top),
-                    (fw, z_bot),
-                    (0, 0, 200),
-                    -1,
-                )
-            else:
-                cv2.rectangle(
-                    overlay_zone,
-                    (z_left, 0),
-                    (z_right, fh),
-                    (0, 0, 200),
-                    -1,
-                )
-            annotator_frame = cv2.addWeighted(
-                overlay_zone, 0.18, annotator_frame, 0.82, 0
-            )
-
-            thickness_base = max(2, int(2 * (max(fw, fh) / 1920)))
-            if is_horizontal:
-                cv2.line(
-                    annotator_frame,
-                    (0, z_top),
-                    (fw, z_top),
-                    (0, 100, 255),
-                    thickness_base,
-                )
-                cv2.line(
-                    annotator_frame,
-                    (0, z_bot),
-                    (fw, z_bot),
-                    (0, 100, 255),
-                    thickness_base,
-                )
-                cv2.line(
-                    annotator_frame,
-                    (0, line_pos),
-                    (fw, line_pos),
-                    (0, 0, 255),
-                    max(3, thickness_base + 1),
-                )
-            else:
-                cv2.line(
-                    annotator_frame,
-                    (z_left, 0),
-                    (z_left, fh),
-                    (0, 100, 255),
-                    thickness_base,
-                )
-                cv2.line(
-                    annotator_frame,
-                    (z_right, 0),
-                    (z_right, fh),
-                    (0, 100, 255),
-                    thickness_base,
-                )
-                cv2.line(
-                    annotator_frame,
-                    (line_pos, 0),
-                    (line_pos, fh),
-                    (0, 0, 255),
-                    max(3, thickness_base + 1),
-                )
-
-            box_w, box_h = 280, 50 + len(SELECTED_CLASS_NAMES) * 28
-            x0, y0 = fw - box_w - 20, 20
-
-            overlay = annotator_frame.copy()
-            cv2.rectangle(overlay, (x0, y0), (x0 + box_w, y0 + box_h), (0, 0, 0), -1)
-            annotator_frame = cv2.addWeighted(overlay, 0.6, annotator_frame, 0.4, 0)
-
-            total = sum(class_counts.values())
-            cv2.putText(
-                annotator_frame,
-                f'Total: {total}',
-                (x0 + 10, y0 + 30),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.8,
-                (0, 255, 0),
-                2,
-            )
-            for i, cls_name in enumerate(SELECTED_CLASS_NAMES):
-                cnt = class_counts.get(cls_name, 0)
-                cv2.putText(
-                    annotator_frame,
-                    f'{cls_name.capitalize()}: {cnt}',
-                    (x0 + 10, y0 + 60 + i * 28),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    0.7,
-                    (255, 255, 255),
-                    2,
-                )
-
-            return annotator_frame
-
-        results = model(frame_infer, verbose=False)[0]
-        detections = sv.Detections.from_ultralytics(results)
-        detections = detections[np.isin(detections.class_id, SELECTED_CLASS_IDS)]
-        detections = byte_tracker.update_with_detections(detections)
-
-        if detections.tracker_id is not None:
-            xyxy = detections.xyxy
-            for i in range(len(detections)):
-                tid = int(detections.tracker_id[i])
-                cls_id = int(detections.class_id[i])
-                cls_name = CLASS_NAMES_DICT[cls_id]
-                cx = (xyxy[i, 0] + xyxy[i, 2]) / 2
-                cy = (xyxy[i, 1] + xyxy[i, 3]) / 2
-
-                if cls_name in SELECTED_CLASS_NAMES and tid not in counted_ids:
-                    if is_horizontal and z_top <= cy <= z_bot:
-                        class_counts[cls_name] = class_counts.get(cls_name, 0) + 1
-                        counted_ids.add(tid)
-                    elif (not is_horizontal) and z_left <= cx <= z_right:
-                        class_counts[cls_name] = class_counts.get(cls_name, 0) + 1
-                        counted_ids.add(tid)
-
-        labels = [
-            f"#{tid} {CLASS_NAMES_DICT[cid]} {conf:0.2f}"
-            for conf, cid, tid in zip(
-                detections.confidence, detections.class_id, detections.tracker_id
-            )
-        ]
-
-        annotator_frame = frame_infer.copy()
-        annotator_frame = trace_annotator.annotate(scene=annotator_frame, detections=detections)
-        annotator_frame = box_annotator.annotate(scene=annotator_frame, detections=detections)
-        annotator_frame = label_annotator.annotate(
-            scene=annotator_frame, detections=detections, labels=labels
-        )
-
-        overlay_zone = annotator_frame.copy()
-        if is_horizontal:
-            cv2.rectangle(
-                overlay_zone,
-                (0, z_top),
-                (fw, z_bot),
-                (0, 0, 200),
-                -1,
-            )
-        else:
-            cv2.rectangle(
-                overlay_zone,
-                (z_left, 0),
-                (z_right, fh),
-                (0, 0, 200),
-                -1,
-            )
-        annotator_frame = cv2.addWeighted(
-            overlay_zone, 0.18, annotator_frame, 0.82, 0
-        )
-
-        thickness_base = max(2, int(2 * (max(fw, fh) / 1920)))
-        if is_horizontal:
-            cv2.line(
-                annotator_frame,
-                (0, z_top),
-                (fw, z_top),
-                (0, 100, 255),
-                thickness_base,
-            )
-            cv2.line(
-                annotator_frame,
-                (0, z_bot),
-                (fw, z_bot),
-                (0, 100, 255),
-                thickness_base,
-            )
-            cv2.line(
-                annotator_frame,
-                (0, line_pos),
-                (fw, line_pos),
-                (0, 0, 255),
-                max(3, thickness_base + 1),
-            )
-        else:
-            cv2.line(
-                annotator_frame,
-                (z_left, 0),
-                (z_left, fh),
-                (0, 100, 255),
-                thickness_base,
-            )
-            cv2.line(
-                annotator_frame,
-                (z_right, 0),
-                (z_right, fh),
-                (0, 100, 255),
-                thickness_base,
-            )
-            cv2.line(
-                annotator_frame,
-                (line_pos, 0),
-                (line_pos, fh),
-                (0, 0, 255),
-                max(3, thickness_base + 1),
-            )
-
-        box_w, box_h = 280, 50 + len(SELECTED_CLASS_NAMES) * 28
-        x0, y0 = fw - box_w - 20, 20
-
-        overlay = annotator_frame.copy()
-        cv2.rectangle(overlay, (x0, y0), (x0 + box_w, y0 + box_h), (0, 0, 0), -1)
-        annotator_frame = cv2.addWeighted(overlay, 0.6, annotator_frame, 0.4, 0)
-
-        total = sum(class_counts.values())
-        cv2.putText(
-            annotator_frame,
-            f'Total: {total}',
-            (x0 + 10, y0 + 30),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.8,
-            (0, 255, 0),
-            2,
-        )
-        for i, cls_name in enumerate(SELECTED_CLASS_NAMES):
-            cnt = class_counts.get(cls_name, 0)
-            cv2.putText(
-                annotator_frame,
-                f'{cls_name.capitalize()}: {cnt}',
-                (x0 + 10, y0 + 60 + i * 28),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.7,
-                (255, 255, 255),
-                2,
-            )
-
-        return annotator_frame
-
-    output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
-    sv.process_video(
-        source_path=video_path,
-        target_path=output_path,
-        callback=callback
-    )
-    return output_path
-
-
-with gr.Blocks(title="Nhận dạng phương tiện giao thông", theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
-    with gr.Row():
-        gr.Markdown(
-            """
-            <div style="display:flex;flex-direction:column;gap:4px;">
-              <h1 style="margin-bottom:4px;">🚗 Nhận diện phương tiện giao thông (YOLOv8 + ByteTrack)</h1>
-              <p style="margin:0;font-size:14px;color:#6b7280;">
-                Thực hiện: <strong>Trần Hải Nam - 223332840</strong>
-              </p>
-            </div>
-            """,
-            elem_id="header",
-        )
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown(
-                "### 🎥 Video đầu vào\n"
-                "Upload video ngắn (ưu tiên &lt; 30s để xử lý nhanh hơn)."
-            )
-            video_input = gr.Video(label="Video đầu vào")
-
-        with gr.Column(scale=1):
-            gr.Markdown(
-                "### ✅ Kết quả đã xử lý\n"
-                "Hiển thị và thống kê số lượng theo lớp."
-            )
-            video_output = gr.Video(label="Video đã xử lý")
-
-    # Tùy chọn cấu hình
-    with gr.Accordion("⚙️ Tùy chọn nâng cao", open=False):
-        with gr.Row():
-            use_resize = gr.Checkbox(
-                value=True, label="Giảm kích thước khung hình trước khi nhận dạng"
-            )
-            line_orientation = gr.Radio(
-                choices=["Ngang", "Dọc"],
-                value="Ngang",
-                label="Hướng phương tiện di chuyển",
-            )
-        with gr.Row():
-            max_frame_size = gr.Slider(
-                minimum=320,
-                maximum=1280,
-                value=DEFAULT_MAX_FRAME_SIZE,
-                step=64,
-                label="Kích thước tối đa (px)",
-            )
-            detect_every_n = gr.Slider(
-                minimum=1,
-                maximum=5,
-                value=DEFAULT_DETECT_EVERY_N_FRAMES,
-                step=1,
-                label="Detect mỗi N frame (1 = mọi frame)",
-            )
-            zone_margin = gr.Slider(
-                minimum=0.02,
-                maximum=0.30,
-                value=DEFAULT_ZONE_MARGIN,
-                step=0.01,
-                label="Độ dày vùng đếm quanh line",
-            )
-
-    btn = gr.Button("▶️ Xử lý video", variant="primary")
-    btn.click(
-        fn=process_video,
-        inputs=[video_input, use_resize, max_frame_size, detect_every_n, line_orientation, zone_margin],
-        outputs=video_output,
-    )
-
-    gr.Markdown(
-        """
-    ---
-    ### ℹ️ Gợi ý sử dụng
-    - Mặc định hướng phương tiện di chuyển để nhận dạng là nằm **ngang** ở giữa khung hình (50% chiều cao).
-    - Có thể chuyển sang hướng **dọc** trong phần _"Tùy chọn nâng cao"_.
-    - Vì sử dụng CPU, nên:
-      - Dùng video **ngắn** (&lt; 30 giây).
-      - Tăng `Detect mỗi N frame` nếu muốn xử lý nhanh hơn.
-    - Model sử dụng: **YOLOv8n**.
-    """
-    )
-
-if __name__ == "__main__":
-    demo.launch()