Spaces:
Sleeping
Sleeping
Old video removal mechanism.
Browse files- app.py +32 -2
- utils/video.py +32 -0
app.py
CHANGED
@@ -10,11 +10,34 @@ from tqdm import tqdm
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from inference.models import YOLOWorld
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from utils.efficient_sam import load, inference_with_boxes
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from utils.video import
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MARKDOWN = """
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# YOLO-World + EfficientSAM 🔥
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This is a demo of zero-shot object detection and instance segmentation using
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[YOLO-World](https://github.com/AILab-CVC/YOLO-World) and
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[EfficientSAM](https://github.com/yformer/EfficientSAM).
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@@ -35,6 +58,7 @@ RESULTS = "results"
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IMAGE_EXAMPLES = [
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['https://media.roboflow.com/dog.jpeg', 'dog, eye, nose, tongue, car', 0.005, 0.1, True, False, False],
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]
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VIDEO_EXAMPLES = [
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['https://media.roboflow.com/supervision/video-examples/croissant-1280x720.mp4', 'croissant', 0.01, 0.2, False, False, False],
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@@ -51,7 +75,7 @@ BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
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MASK_ANNOTATOR = sv.MaskAnnotator()
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LABEL_ANNOTATOR = sv.LabelAnnotator()
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create_directory(directory_path=RESULTS)
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@@ -89,6 +113,9 @@ def process_image(
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with_confidence: bool = False,
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with_class_agnostic_nms: bool = False,
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) -> np.ndarray:
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categories = process_categories(categories)
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YOLO_WORLD_MODEL.set_classes(categories)
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results = YOLO_WORLD_MODEL.infer(input_image, confidence=confidence_threshold)
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@@ -124,6 +151,9 @@ def process_video(
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with_class_agnostic_nms: bool = False,
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progress=gr.Progress(track_tqdm=True)
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) -> str:
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categories = process_categories(categories)
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YOLO_WORLD_MODEL.set_classes(categories)
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video_info = sv.VideoInfo.from_video_path(input_video)
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from inference.models import YOLOWorld
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from utils.efficient_sam import load, inference_with_boxes
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from utils.video import (
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generate_file_name,
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calculate_end_frame_index,
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create_directory,
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remove_files_older_than
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)
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MARKDOWN = """
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# YOLO-World + EfficientSAM 🔥
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<div>
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<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-yolo-world.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;">
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</a>
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<a href="https://blog.roboflow.com/what-is-yolo-world/">
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<img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="Roboflow" style="display:inline-block;">
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</a>
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<a href="https://www.youtube.com/watch?v=X7gKBGVz4vs">
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<img src="https://badges.aleen42.com/src/youtube.svg" alt="YouTube" style="display:inline-block;">
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</a>
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<a href="https://github.com/AILab-CVC/YOLO-World">
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<img src="https://badges.aleen42.com/src/github.svg" alt="GitHub" style="display:inline-block;">
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</a>
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<a href="https://arxiv.org/abs/2401.17270">
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<img src="https://img.shields.io/badge/arXiv-2401.17270-b31b1b.svg" alt="arXiv" style="display:inline-block;">
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</a>
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</div>
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This is a demo of zero-shot object detection and instance segmentation using
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[YOLO-World](https://github.com/AILab-CVC/YOLO-World) and
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[EfficientSAM](https://github.com/yformer/EfficientSAM).
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IMAGE_EXAMPLES = [
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['https://media.roboflow.com/dog.jpeg', 'dog, eye, nose, tongue, car', 0.005, 0.1, True, False, False],
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['https://media.roboflow.com/albert-4x.png', 'hand, hair', 0.005, 0.1, True, False, False],
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]
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VIDEO_EXAMPLES = [
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['https://media.roboflow.com/supervision/video-examples/croissant-1280x720.mp4', 'croissant', 0.01, 0.2, False, False, False],
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MASK_ANNOTATOR = sv.MaskAnnotator()
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LABEL_ANNOTATOR = sv.LabelAnnotator()
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# creating video results directory
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create_directory(directory_path=RESULTS)
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with_confidence: bool = False,
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with_class_agnostic_nms: bool = False,
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) -> np.ndarray:
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# cleanup of old video files
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remove_files_older_than(RESULTS, 30)
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categories = process_categories(categories)
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YOLO_WORLD_MODEL.set_classes(categories)
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results = YOLO_WORLD_MODEL.infer(input_image, confidence=confidence_threshold)
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with_class_agnostic_nms: bool = False,
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progress=gr.Progress(track_tqdm=True)
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) -> str:
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# cleanup of old video files
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remove_files_older_than(RESULTS, 30)
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categories = process_categories(categories)
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YOLO_WORLD_MODEL.set_classes(categories)
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video_info = sv.VideoInfo.from_video_path(input_video)
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utils/video.py
CHANGED
@@ -1,6 +1,7 @@
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import os
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import datetime
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import uuid
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import supervision as sv
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@@ -14,6 +15,37 @@ def generate_file_name(extension="mp4"):
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return f"{current_datetime}_{unique_id}.{extension}"
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def calculate_end_frame_index(source_video_path: str) -> int:
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video_info = sv.VideoInfo.from_video_path(source_video_path)
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return min(
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import os
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import datetime
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import uuid
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from typing import List
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import supervision as sv
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return f"{current_datetime}_{unique_id}.{extension}"
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def list_files_older_than(directory: str, diff_minutes: int) -> List[str]:
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diff_seconds = diff_minutes * 60
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now = datetime.datetime.now()
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older_files: List[str] = []
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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if os.path.isfile(file_path):
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file_mod_time = os.path.getmtime(file_path)
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file_mod_datetime = datetime.datetime.fromtimestamp(file_mod_time)
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time_diff = now - file_mod_datetime
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if time_diff.total_seconds() > diff_seconds:
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older_files.append(file_path)
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return older_files
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def remove_files_older_than(directory: str, diff_minutes: int) -> None:
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older_files = list_files_older_than(directory, diff_minutes)
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file_count = len(older_files)
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for file_path in older_files:
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os.remove(file_path)
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now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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print(
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f"[{now}] Removed {file_count} files older than {diff_minutes} minutes from "
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f"'{directory}' directory."
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)
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def calculate_end_frame_index(source_video_path: str) -> int:
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video_info = sv.VideoInfo.from_video_path(source_video_path)
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return min(
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