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import os |
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import os.path as osp |
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from typing import List |
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import gradio as gr |
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import numpy as np |
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import supervision as sv |
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import torch |
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from PIL import Image |
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from supervision import Color |
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from ultralytics import YOLO |
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MARKDOWN = """ |
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<h1 style="text-align: center;">YOLO Orang Outan Detection 🦧🔍 </h1> |
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## About the model 👁️ |
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This is a demo for my YOLOv8 nano trained for orang outan detection.\\ |
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The model was trained using [this dataset](https://images.cv/dataset/orangutan-image-classification-dataset) |
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for orang outan images and [this dataset](https://www.kaggle.com/datasets/slothkong/10-monkey-species/data) as background images. Annotations were obtained using zero shot object detection method GroundingDino.\\ |
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The code can be found on my github repository: https://github.com/clementapa/orang-outan-image-video-detection. |
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## About the orang outans 🦧 |
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Because to habitat destruction, illicit poaching, and the pet trade, orangutans are in danger of going extinct. Their natural habitat has been significantly reduced by deforestation and the growth of palm oil plantations. Adult orangutans are occasionally sought for their body parts, and they are frequently captured and sold as pets. Climate change and disease are also taking a toll on their populations. Furthermore, it is concerning to note that they are limited to Borneo and Sumatra, two places on Earth. Sustainable practises and conservation initiatives are crucial to preventing the permanent extinction of these amazing animals. |
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## AI for good 🌍 |
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Artificial Intelligence (AI) has unquestionable power in the realm of innovation and technology. Even though artificial intelligence (AI) has frequently been used for commercial advantage, it is important to stress that AI can also be used for more noble purposes, such as protecting the environment and the planet's future. We can build a more promising and sustainable future if we reorient AI's focus from business to improving our planet. |
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""" |
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EXAMPLES = [] |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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YOLO_MODEL = YOLO("train_7best.pt") |
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BOX_ANNOTATOR = sv.BoxAnnotator(color=Color.from_hex("#FF00E4")) |
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def annotate( |
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image_bgr_numpy: Image.Image, |
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detections: sv.Detections, |
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annotator: sv.BoxAnnotator, |
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labels: str, |
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) -> Image.Image: |
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thickness = 2 |
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text_thickness = 1 |
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text_scale = 1.0 |
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height, width, _ = image_bgr_numpy.shape |
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thickness_ratio = ((width + height) / 2) / 400 |
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text_scale_ratio = ((width + height) / 2) / 600 |
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text_thickness_ratio = ((width + height) / 2) / 400 |
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annotator.thickness = int(thickness * thickness_ratio) |
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annotator.text_scale = float(text_scale * text_scale_ratio) |
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annotator.text_thickness = int(text_thickness * text_thickness_ratio) |
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annotated_bgr_image = annotator.annotate( |
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scene=image_bgr_numpy, detections=detections, labels=labels |
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) |
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return Image.fromarray(annotated_bgr_image[:, :, ::-1]) |
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def inference_image(image_rgb_pil: Image.Image, confidence: float) -> List[Image.Image]: |
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output = YOLO_MODEL(image_rgb_pil, imgsz=640, verbose=False)[0] |
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detections = sv.Detections.from_ultralytics(output) |
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detections = detections[detections.confidence >= confidence] |
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labels = [ |
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f"{output.names[class_id]} {confidence:0.2f}" |
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for _, _, confidence, class_id, _ in detections |
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] |
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return annotate( |
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image_bgr_numpy=output.orig_img.copy(), |
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detections=detections, |
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annotator=BOX_ANNOTATOR, |
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labels=labels, |
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) |
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def process_frame(frame: np.ndarray, _) -> np.ndarray: |
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output = YOLO_MODEL(frame, imgsz=640, verbose=False)[0] |
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detections = sv.Detections.from_ultralytics(output) |
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labels = [ |
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f"{output.names[class_id]} {confidence:0.2f}" |
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for _, _, confidence, class_id, _ in detections |
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] |
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thickness = 2 |
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text_thickness = 1 |
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text_scale = 1.0 |
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height, width, _ = output.orig_img.shape |
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thickness_ratio = ((width + height) / 2) / 400 |
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text_scale_ratio = ((width + height) / 2) / 600 |
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text_thickness_ratio = ((width + height) / 2) / 400 |
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BOX_ANNOTATOR.thickness = int(thickness * thickness_ratio) |
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BOX_ANNOTATOR.text_scale = float(text_scale * text_scale_ratio) |
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BOX_ANNOTATOR.text_thickness = int(text_thickness * text_thickness_ratio) |
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annotated_frame = BOX_ANNOTATOR.annotate( |
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scene=output.orig_img.copy(), detections=detections, labels=labels |
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) |
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return annotated_frame |
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def inference_video(path_video): |
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path_output_video = "temp.mp4" |
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sv.process_video( |
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source_path=path_video, |
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target_path=path_output_video, |
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callback=process_frame, |
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) |
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return path_output_video |
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custom_theme = gr.themes.Soft(primary_hue="green") |
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with gr.Blocks(theme=custom_theme, css="style.css") as demo: |
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gr.Markdown(MARKDOWN) |
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with gr.Tab("Detect on an image 🖼️"): |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image( |
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image_mode="RGB", |
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sources=["upload", "clipboard"], |
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type="pil", |
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) |
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example_folder = osp.join( |
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osp.dirname(__file__), "./resources/examples_images" |
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) |
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example_fns = [ |
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osp.join(example_folder, example) |
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for example in os.listdir(example_folder) |
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] |
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gr.Examples( |
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examples=example_fns, |
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inputs=[input_image], |
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outputs=[input_image], |
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cache_examples=False, |
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label="Examples (click one of the images below to start)", |
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examples_per_page=10, |
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) |
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confidence_image_slider = gr.Slider( |
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label="Confidence", minimum=0.1, maximum=1.0, step=0.05, value=0.6 |
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) |
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submit_button_image = gr.Button("Let's find orang outans 🦧 !") |
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output_image = gr.Image(label="Results", type="pil") |
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with gr.Tab("Detect on a video 📹"): |
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with gr.Row(): |
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with gr.Column(): |
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input_video = gr.Video(sources=["upload"]) |
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example_folder = osp.join( |
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osp.dirname(__file__), "./resources/examples_videos" |
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) |
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example_fns = [ |
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osp.join(example_folder, example) |
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for example in os.listdir(example_folder) |
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] |
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gr.Examples( |
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examples=example_fns, |
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inputs=[input_video], |
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outputs=[input_video], |
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cache_examples=False, |
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label="Examples (click one of the images below to start)", |
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examples_per_page=10, |
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) |
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submit_button_video = gr.Button("Let's find orang outans 🦧 !") |
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output_video = gr.Video(label="Results") |
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submit_button_image.click( |
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inference_image, |
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inputs=[input_image, confidence_image_slider], |
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outputs=output_image, |
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queue=True, |
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) |
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submit_button_video.click( |
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inference_video, |
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inputs=[input_video], |
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outputs=output_video, |
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queue=True, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20, api_open=False).launch() |
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