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| from transformers import AutoFeatureExtractor, YolosForObjectDetection | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| import matplotlib.pyplot as plt | |
| import io | |
| import numpy as np | |
| COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], | |
| [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]] | |
| def process_class_list(classes_string: str): | |
| return [x.strip() for x in classes_string.split(",")] if classes_string else [] | |
| def model_inference(img, model_name: str, prob_threshold: int, classes_to_show = str): | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(f"hustvl/{model_name}") | |
| model = YolosForObjectDetection.from_pretrained(f"hustvl/{model_name}") | |
| img = Image.fromarray(img) | |
| pixel_values = feature_extractor(img, return_tensors="pt").pixel_values | |
| with torch.no_grad(): | |
| outputs = model(pixel_values, output_attentions=True) | |
| probas = outputs.logits.softmax(-1)[0, :, :-1] | |
| keep = probas.max(-1).values > prob_threshold | |
| target_sizes = torch.tensor(img.size[::-1]).unsqueeze(0) | |
| postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes) | |
| bboxes_scaled = postprocessed_outputs[0]['boxes'] | |
| classes_list = process_class_list(classes_to_show) | |
| return plot_results( | |
| img, probas[keep], bboxes_scaled[keep], model, classes_list | |
| ) | |
| def plot_results(pil_img, prob, boxes, model, classes_list): | |
| plt.figure(figsize=(16,10)) | |
| plt.imshow(pil_img) | |
| ax = plt.gca() | |
| colors = COLORS * 100 | |
| for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors): | |
| cl = p.argmax() | |
| object_class = model.config.id2label[cl.item()] | |
| if len(classes_list) > 0 : | |
| if object_class not in classes_list: | |
| continue | |
| ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, | |
| fill=False, color=c, linewidth=3)) | |
| text = f'{object_class}: {p[cl]:0.2f}' | |
| ax.text(xmin, ymin, text, fontsize=15, | |
| bbox=dict(facecolor='yellow', alpha=0.5)) | |
| plt.axis('off') | |
| return fig2img(plt.gcf()) | |
| def fig2img(fig): | |
| buf = io.BytesIO() | |
| fig.savefig(buf) | |
| buf.seek(0) | |
| return Image.open(buf) | |
| description = """ | |
| Do you want to see what objects are in your images? Try our object detection app, powered by YOLOS, a state-of-the-art algorithm that can find and name multiple objects in a single image. | |
| You can upload or drag and drop an image file to detect objects using YOLOS models. | |
| You can also choose from different YOLOS models, adjust the probability threshold, and select the classes to use for detection. | |
| Our app will show you the results in an interactive image with bounding boxes and labels for each detected object. | |
| You can also download the results as an image file. Our app is fast, accurate, and easy to use. | |
| Try it now and discover the power of object detection! 😊 | |
| """ | |
| image_in = gr.components.Image() | |
| image_out = gr.components.Image() | |
| model_choice = gr.components.Dropdown(["yolos-tiny", "yolos-small", "yolos-base", "yolos-small-300", "yolos-small-dwr"], value="yolos-small", label="YOLOS Model") | |
| prob_threshold_slider = gr.components.Slider(minimum=0, maximum=1.0, step=0.01, value=0.9, label="Probability Threshold") | |
| classes_to_show = gr.components.Textbox(placeholder="e.g. person, car , laptop", label="Classes to use (Optional)") | |
| Iface = gr.Interface( | |
| fn=model_inference, | |
| inputs=[image_in,model_choice, prob_threshold_slider, classes_to_show], | |
| outputs=image_out, | |
| title="Object Detection With YOLO", | |
| description=description, | |
| theme='HaleyCH/HaleyCH_Theme', | |
| ).launch() |