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| import gradio as gr | |
| import sahi.utils | |
| from sahi import AutoDetectionModel | |
| import sahi.predict | |
| import sahi.slicing | |
| from PIL import Image | |
| import numpy | |
| IMAGE_SIZE = 640 | |
| # Images | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg", | |
| "apple_tree.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg", | |
| "highway.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg", | |
| "highway2.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg", | |
| "highway3.jpg", | |
| ) | |
| # Model | |
| model = AutoDetectionModel.from_pretrained( | |
| model_type="yolov5", model_path="yolov5s6.pt", device="cpu", confidence_threshold=0.5, image_size=IMAGE_SIZE | |
| ) | |
| def sahi_yolo_inference( | |
| image, | |
| slice_height=512, | |
| slice_width=512, | |
| overlap_height_ratio=0.2, | |
| overlap_width_ratio=0.2, | |
| postprocess_type="NMS", | |
| postprocess_match_metric="IOU", | |
| postprocess_match_threshold=0.5, | |
| postprocess_class_agnostic=False, | |
| ): | |
| image_width, image_height = image.size | |
| sliced_bboxes = sahi.slicing.get_slice_bboxes( | |
| image_height, | |
| image_width, | |
| slice_height, | |
| slice_width, | |
| False, | |
| overlap_height_ratio, | |
| overlap_width_ratio, | |
| ) | |
| if len(sliced_bboxes) > 60: | |
| raise ValueError( | |
| f"{len(sliced_bboxes)} slices are too much for huggingface spaces, try smaller slice size." | |
| ) | |
| # standard inference | |
| prediction_result_1 = sahi.predict.get_prediction( | |
| image=image, detection_model=model | |
| ) | |
| print(image) | |
| visual_result_1 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_1.object_prediction_list, | |
| ) | |
| output_1 = Image.fromarray(visual_result_1["image"]) | |
| # sliced inference | |
| prediction_result_2 = sahi.predict.get_sliced_prediction( | |
| image=image, | |
| detection_model=model, | |
| slice_height=int(slice_height), | |
| slice_width=int(slice_width), | |
| overlap_height_ratio=overlap_height_ratio, | |
| overlap_width_ratio=overlap_width_ratio, | |
| postprocess_type=postprocess_type, | |
| postprocess_match_metric=postprocess_match_metric, | |
| postprocess_match_threshold=postprocess_match_threshold, | |
| postprocess_class_agnostic=postprocess_class_agnostic, | |
| ) | |
| visual_result_2 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_2.object_prediction_list, | |
| ) | |
| output_2 = Image.fromarray(visual_result_2["image"]) | |
| return output_1, output_2 | |
| inputs = [ | |
| gr.Image(type="pil", label="Original Image"), | |
| gr.Number(default=512, label="slice_height"), | |
| gr.Number(default=512, label="slice_width"), | |
| gr.Number(default=0.2, label="overlap_height_ratio"), | |
| gr.Number(default=0.2, label="overlap_width_ratio"), | |
| gr.Dropdown( | |
| ["NMS", "GREEDYNMM"], | |
| type="value", | |
| value="NMS", | |
| label="postprocess_type", | |
| ), | |
| gr.Dropdown( | |
| ["IOU", "IOS"], type="value", default="IOU", label="postprocess_type" | |
| ), | |
| gr.Number(default=0.5, label="postprocess_match_threshold"), | |
| gr.Checkbox(default=True, label="postprocess_class_agnostic"), | |
| ] | |
| outputs = [ | |
| gr.Image(type="pil", label="YOLOv5s"), | |
| gr.Image(type="pil", label="YOLOv5s + SAHI"), | |
| ] | |
| title = "Small Object Detection with SAHI + YOLOv5" | |
| description = "SAHI + YOLOv5 demo for small object detection. Upload an image or click an example image to use." | |
| article = "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>" | |
| examples = [ | |
| ["apple_tree.jpg", 256, 256, 0.2, 0.2, "NMS", "IOU", 0.4, True], | |
| ["highway.jpg", 256, 256, 0.2, 0.2, "NMS", "IOU", 0.4, True], | |
| ["highway2.jpg", 512, 512, 0.2, 0.2, "NMS", "IOU", 0.4, True], | |
| ["highway3.jpg", 512, 512, 0.2, 0.2, "NMS", "IOU", 0.4, True], | |
| ] | |
| gr.Interface( | |
| sahi_yolo_inference, | |
| inputs, | |
| outputs, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| theme="huggingface", | |
| cache_examples=True, | |
| ).launch(debug=True, enable_queue=True) | |