app.py

import os
import cv2
import tqdm
import uuid
import logging

import torch
import spaces
import trackers
import numpy as np
import gradio as gr
import imageio.v3 as iio
import supervision as sv

from pathlib import Path
from functools import lru_cache
from typing import List, Optional, Tuple

from transformers import AutoModelForObjectDetection, AutoImageProcessor

# Configuration constants
CHECKPOINTS = [
    "ustc-community/dfine-xlarge-obj2coco"
]
DEFAULT_CHECKPOINT = CHECKPOINTS[0]
DEFAULT_CONFIDENCE_THRESHOLD = 0.3

TORCH_DTYPE = torch.float32

# Video
MAX_NUM_FRAMES = 250
BATCH_SIZE = 4
ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".avi", ".mov"}
VIDEO_OUTPUT_DIR = Path("static/videos")
VIDEO_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)


class TrackingAlgorithm:
    BYTETRACK = "ByteTrack (2021)"
    DEEPSORT = "DeepSORT (2017)"
    SORT = "SORT (2016)"


# Create a color palette for visualization
# These hex color codes define different colors for tracking different objects
color = sv.ColorPalette.from_hex([
    "#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
    "#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00"
])

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


@lru_cache(maxsize=3)
def get_model_and_processor(checkpoint: str):
    model = AutoModelForObjectDetection.from_pretrained(checkpoint, torch_dtype=TORCH_DTYPE)
    image_processor = AutoImageProcessor.from_pretrained(checkpoint)
    return model, image_processor


@spaces.GPU(duration=20)
def detect_objects(
        images: List[np.ndarray] | np.ndarray,
        target_size: Optional[Tuple[int, int]] = None,
        batch_size: int = BATCH_SIZE
):
    checkpoint = "ustc-community/dfine-xlarge-obj2coco"
    confidence_threshold = 0.3
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model, image_processor = get_model_and_processor(checkpoint)
    model = model.to(device)
    classes = ["person","aeroplane","bicycle","car","motorbike","bus","train","truck","boat"]
    if classes is not None:
        wrong_classes = [cls for cls in classes if cls not in model.config.label2id]
        if wrong_classes:
            gr.Warning(f"Classes not found in model config")
        keep_ids = [model.config.label2id[cls] for cls in classes if cls in model.config.label2id]
    else:
        keep_ids = None

    if isinstance(images, np.ndarray) and images.ndim == 4:
        images = [x for x in images]

    batches = [images[i:i + batch_size] for i in range(0, len(images), batch_size)]

    results = []
    for batch in tqdm.tqdm(batches, desc="Processing frames"):

        # preprocess images
        inputs = image_processor(images=batch, return_tensors="pt")
        inputs = inputs.to(device).to(TORCH_DTYPE)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # postprocess outputs
        if target_size:
            target_sizes = [target_size] * len(batch)
        else:
            target_sizes = [(image.shape[0], image.shape[1]) for image in batch]

        batch_results = image_processor.post_process_object_detection(
            outputs, target_sizes=target_sizes, threshold=confidence_threshold
        )

        results.extend(batch_results)

    # move results to cpu
    for i, result in enumerate(results):
        results[i] = {k: v.cpu() for k, v in result.items()}
        if keep_ids is not None:
            keep = torch.isin(results[i]["labels"], torch.tensor(keep_ids))
            results[i] = {k: v[keep] for k, v in results[i].items()}

    return results, model.config.id2label


def get_target_size(image_height, image_width, max_size: int):
    if image_height < max_size and image_width < max_size:
        new_height, new_width = image_height, image_width
    elif image_height > image_width:
        new_height = max_size
        new_width = int(image_width * max_size / image_height)
    else:
        new_width = max_size
        new_height = int(image_height * max_size / image_width)

    # make even (for video codec compatibility)
    new_height = new_height // 2 * 2
    new_width = new_width // 2 * 2

    return new_width, new_height


def read_video_k_frames(video_path: str, k: int, read_every_i_frame: int = 1):
    cap = cv2.VideoCapture(video_path)
    frames = []
    i = 0
    progress_bar = tqdm.tqdm(total=k, desc="Reading frames")
    while cap.isOpened() and len(frames) < k:
        ret, frame = cap.read()
        if not ret:
            break
        if i % read_every_i_frame == 0:
            frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            progress_bar.update(1)
        i += 1
    cap.release()
    progress_bar.close()
    return frames


def get_tracker(fps: float):
    tracker = TrackingAlgorithm.BYTETRACK
    if tracker == TrackingAlgorithm.SORT:
        return trackers.SORTTracker(frame_rate=fps)
    elif tracker == TrackingAlgorithm.DEEPSORT:
        feature_extractor = trackers.DeepSORTFeatureExtractor.from_timm("mobilenetv4_conv_small.e1200_r224_in1k",
                                                                        device="cpu")
        return trackers.DeepSORTTracker(feature_extractor, frame_rate=fps)
    elif tracker == TrackingAlgorithm.BYTETRACK:
        return sv.ByteTrack(frame_rate=int(fps))
    else:
        raise ValueError(f"Invalid tracker: {tracker}")


def update_tracker(tracker, detections, frame):
    tracker_name = tracker.__class__.__name__
    if tracker_name == "SORTTracker":
        return tracker.update(detections)
    elif tracker_name == "DeepSORTTracker":
        return tracker.update(detections, frame)
    elif tracker_name == "ByteTrack":
        return tracker.update_with_detections(detections)
    else:
        raise ValueError(f"Invalid tracker: {tracker}")


def process_video(
        video_path: str,
        tracker_algorithm: Optional[str] = None,
        progress: gr.Progress = gr.Progress(track_tqdm=True),
) -> str:
    if not video_path or not os.path.isfile(video_path):
        raise ValueError(f"Invalid video path: {video_path}")

    ext = os.path.splitext(video_path)[1].lower()
    if ext not in ALLOWED_VIDEO_EXTENSIONS:
        raise ValueError(f"Unsupported video format: {ext}, supported formats: {ALLOWED_VIDEO_EXTENSIONS}")

    video_info = sv.VideoInfo.from_video_path(video_path)
    read_each_i_frame = max(1, video_info.fps // 25)
    target_fps = video_info.fps / read_each_i_frame
    target_width, target_height = get_target_size(video_info.height, video_info.width, 1080)

    n_frames_to_read = min(MAX_NUM_FRAMES, video_info.total_frames // read_each_i_frame)
    frames = read_video_k_frames(video_path, n_frames_to_read, read_each_i_frame)
    frames = [cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_CUBIC) for frame in frames]

    # Set the color lookup mode to assign colors by track ID
    # This mean objects with the same track ID will be annotated by the same color
    color_lookup = sv.ColorLookup.TRACK if tracker_algorithm else sv.ColorLookup.CLASS

    box_annotator = sv.BoxAnnotator(color, color_lookup=color_lookup, thickness=1)
    label_annotator = sv.LabelAnnotator(color, color_lookup=color_lookup, text_scale=0.5)
    trace_annotator = sv.TraceAnnotator(color, color_lookup=color_lookup, thickness=1, trace_length=100)


    results, id2label = detect_objects(
        images=np.array(frames),
        target_size=(target_height, target_width),
    )

    annotated_frames = []

    # detections
    if tracker_algorithm:
        tracker = get_tracker(tracker_algorithm, target_fps)
        for frame, result in progress.tqdm(zip(frames, results), desc="Tracking objects", total=len(frames)):
            detections = sv.Detections.from_transformers(result, id2label=id2label)
            detections = detections.with_nms(threshold=0.95, class_agnostic=True)
            detections = update_tracker(tracker, detections, frame)
            labels = [f"#{tracker_id} {id2label[class_id]}" for class_id, tracker_id in
                      zip(detections.class_id, detections.tracker_id)]
            annotated_frame = box_annotator.annotate(scene=frame, detections=detections)
            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
            annotated_frame = trace_annotator.annotate(scene=annotated_frame, detections=detections)
            annotated_frames.append(annotated_frame)

    else:
        for frame, result in tqdm.tqdm(zip(frames, results), desc="Annotating frames", total=len(frames)):
            detections = sv.Detections.from_transformers(result, id2label=id2label)
            detections = detections.with_nms(threshold=0.95, class_agnostic=True)
            annotated_frame = box_annotator.annotate(scene=frame, detections=detections)
            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections)
            annotated_frames.append(annotated_frame)

    output_filename = os.path.join(VIDEO_OUTPUT_DIR, f"output_{uuid.uuid4()}.mp4")
    iio.imwrite(output_filename, annotated_frames, fps=target_fps, codec="h264")
    return output_filename


def create_video_inputs() -> List[gr.components.Component]:
    return [
        gr.Video(
            label="Upload Video",
            sources=["upload"],
            interactive=True,
            format="mp4",  # Ensure MP4 format
            elem_classes="input-component",
        )
    ]


def create_button_row() -> List[gr.Button]:
    return [
        gr.Button(
            f"Detect Objects", variant="primary", elem_classes="action-button"
        ),
        gr.Button(f"Clear", variant="secondary", elem_classes="action-button"),
    ]


# Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # Vehicle & People Detection Demo
        ## Input your video and see the detected!
        """,
        elem_classes="header-text",
    )

    with gr.Tabs():
        with gr.Tab("Video"):
            gr.Markdown(
                f"The input video will be processed in ~25 FPS (up to {MAX_NUM_FRAMES} frames in result)."
            )
            with gr.Row():
                with gr.Column(scale=1, min_width=300):
                    with gr.Group():
                        video_input = create_video_inputs()[0]
                        video_detect_button, video_clear_button = create_button_row()
                with gr.Column(scale=2):
                    video_output = gr.Video(
                        label="Detection Results",
                        format="mp4",  # Explicit MP4 format
                        elem_classes="output-component",
                    )
    video_clear_button.click(
        fn=lambda: (None,None),
        outputs=[
            video_input,
            video_output
        ]
    )
    video_detect_button.click(
        fn=process_video,
        inputs=[video_input],
        outputs=[video_output],
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()