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| import io | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import requests, validators | |
| import torch | |
| from PIL import Image | |
| from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection | |
| import os | |
| os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" | |
| # colors for visualization | |
| 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] | |
| ] | |
| title = 'Object Detection App with DETR and YOLOS' | |
| description = """ | |
| Links to HuggingFace Models: | |
| - [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) | |
| - [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) | |
| - [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) | |
| """ | |
| models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small'] | |
| options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True) | |
| def make_prediction(img, feature_extractor, model): | |
| inputs = feature_extractor(img, return_tensors="pt") | |
| outputs = model(**inputs) | |
| img_size = torch.tensor([tuple(reversed(img.size))]) | |
| processed_outputs = feature_extractor.post_process(outputs, img_size) | |
| return processed_outputs[0] | |
| def fig2img(fig): | |
| buf = io.BytesIO() | |
| fig.savefig(buf) | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None): | |
| keep = output_dict["scores"] > threshold | |
| boxes = output_dict["boxes"][keep].tolist() | |
| scores = output_dict["scores"][keep].tolist() | |
| labels = output_dict["labels"][keep].tolist() | |
| if id2label is not None: | |
| labels = [id2label[x] for x in labels] | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(pil_img) | |
| ax = plt.gca() | |
| colors = COLORS * 100 | |
| for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): | |
| ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) | |
| ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) | |
| plt.axis("off") | |
| return fig2img(plt.gcf()) | |
| def detect_objects(model_name,url,image_upload,threshold): | |
| #Extract model and feature extractor | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) | |
| if 'detr' in model_name: | |
| model = DetrForObjectDetection.from_pretrained(model_name) | |
| elif 'yolos' in model_name: | |
| model = YolosForObjectDetection.from_pretrained(model_name) | |
| if validators.url(url): | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| elif image_upload: | |
| image = image_upload | |
| #Make prediction | |
| processed_outputs = make_prediction(image, feature_extractor, model) | |
| #Visualize prediction | |
| viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) | |
| return viz_img | |
| gr.Interface( | |
| fn = detect_objects, | |
| inputs = [options, | |
| gr.Textbox(lines=1,label='Enter valid image URL here..'), | |
| gr.Image(type='pil'), | |
| gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')], | |
| outputs=gr.Image(shape=(400,400)), | |
| title = title, | |
| description=description | |
| ).launch() |