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| import os | |
| import supervision as sv | |
| import cv2 | |
| from ultralytics import YOLO | |
| import gradio as gr | |
| import numpy | |
| #HOME = os.getcwd() | |
| # print(HOME) | |
| # Description | |
| title = "<center><strong><font size='8'>Semantic Detection<font></strong></center>" | |
| description_e = """This is a demo of [Semantic Detection Model](https://github.com/phchamai/semantic_detection). | |
| Enjoy! | |
| """ | |
| examples = [ | |
| ["assets/sample1.jpeg"], | |
| # ["assets/sample2.jpg"], | |
| ] | |
| default_example = examples[0] | |
| css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }" | |
| #image = cv2.imread(IMAGE_PATH) | |
| model = YOLO("yolov8m.pt") | |
| def segment_everything( | |
| image | |
| ): | |
| result = model(image, verbose=False)[0] | |
| detections = sv.Detections.from_ultralytics(result) | |
| #print(detections) | |
| # model_seg = YOLO("yolov8m-seg.pt") | |
| # result = model_seg(image, verbose=False)[0] | |
| # detections_segmentation = sv.Detections.from_ultralytics(result) | |
| # print(detections_segmentation) | |
| detections_filtered = detections[detections.confidence > 0.7] | |
| box_annotator = sv.BoxAnnotator() | |
| label_annotator = sv.LabelAnnotator() | |
| labels = [] | |
| labels_count = {} | |
| for class_id, confidence in zip(detections_filtered.class_id, detections_filtered.confidence): | |
| class_name = model.model.names[class_id] | |
| labels_count[class_name] = labels_count.get(class_name, 0) + 1 | |
| labels.append(f"{class_name} ") # {confidence:.2f} | |
| #print(labels_count) | |
| annotated_image = box_annotator.annotate( | |
| image.copy(), | |
| detections=detections_filtered, | |
| ) | |
| annotated_image = label_annotator.annotate( | |
| annotated_image, detections=detections_filtered, labels=labels | |
| ) | |
| #sv.plot_image(image=annotated_image, size=(8, 8)) | |
| return annotated_image, str(labels_count) | |
| cond_img_e = gr.Image(label="Input", value=default_example[0], type="pil") | |
| segm_img_e = gr.Image(label="Output Image", interactive=False, type="pil") | |
| with gr.Blocks(css=css, title="Sematic Detection") as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Title | |
| gr.Markdown(title) | |
| # Description | |
| gr.Markdown(description_e) | |
| with gr.Tab("Everything mode"): | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=0.5): | |
| cond_img_e.render() | |
| with gr.Column(scale=0.5): | |
| segm_img_e.render() | |
| # Submit & Clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| segment_btn_e = gr.Button( | |
| "Start", variant="primary" | |
| ) | |
| clear_btn_e = gr.Button("Clear", variant="secondary") | |
| with gr.Column(): | |
| out_result = gr.TextArea(label="Output") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[cond_img_e], | |
| outputs=[segm_img_e, out_result], | |
| fn=segment_everything, | |
| cache_examples=True, | |
| examples_per_page=4, | |
| ) | |
| segment_btn_e.click( | |
| segment_everything, | |
| inputs=[ | |
| cond_img_e, | |
| ], | |
| outputs=[segm_img_e, out_result], | |
| ) | |
| def clear(): | |
| return None, None | |
| def clear_text(): | |
| return None, None, None | |
| clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e]) | |
| demo.queue() | |
| demo.launch() |