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import io |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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import requests, validators |
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import torch |
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import pathlib |
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from PIL import Image |
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from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection |
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import os |
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COLORS = [ |
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[0.000, 0.447, 0.741], |
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[0.850, 0.325, 0.098], |
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[0.929, 0.694, 0.125], |
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[0.494, 0.184, 0.556], |
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[0.466, 0.674, 0.188], |
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[0.301, 0.745, 0.933] |
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] |
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def make_prediction(img, feature_extractor, model): |
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inputs = feature_extractor(img, return_tensors="pt") |
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outputs = model(**inputs) |
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img_size = torch.tensor([tuple(reversed(img.size))]) |
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processed_outputs = feature_extractor.post_process(outputs, img_size) |
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return processed_outputs[0] |
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def fig2img(fig): |
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buf = io.BytesIO() |
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fig.savefig(buf) |
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buf.seek(0) |
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img = Image.open(buf) |
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return img |
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def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None): |
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keep = output_dict["scores"] > threshold |
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boxes = output_dict["boxes"][keep].tolist() |
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scores = output_dict["scores"][keep].tolist() |
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labels = output_dict["labels"][keep].tolist() |
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if id2label is not None: |
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labels = [id2label[x] for x in labels] |
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plt.figure(figsize=(16, 10)) |
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plt.imshow(pil_img) |
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ax = plt.gca() |
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colors = COLORS * 100 |
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for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): |
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ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) |
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ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) |
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plt.axis("off") |
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return fig2img(plt.gcf()) |
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models = ["facebook/detr-resnet-50", |
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"facebook/detr-resnet-101", |
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'hustvl/yolos-small', |
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'hustvl/yolos-tiny'] |
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def detect_objects(image_input,threshold): |
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labels = [] |
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feature_extractor_1 = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50") |
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feature_extractor_2 = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-101") |
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feature_extractor_3 = AutoFeatureExtractor.from_pretrained('hustvl/yolos-small') |
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feature_extractor_4 = AutoFeatureExtractor.from_pretrained('hustvl/yolos-tiny') |
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model_1 = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") |
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model_2 = YolosForObjectDetection.from_pretrained('hustvl/yolos-small') |
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model_3 = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101") |
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model_4 = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny') |
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processed_outputs_1 = make_prediction(image_input, feature_extractor_1, model_1) |
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processed_outputs_2 = make_prediction(image_input, feature_extractor_2, model_2) |
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processed_outputs_3 = make_prediction(image_input, feature_extractor_3, model_3) |
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processed_outputs_4 = make_prediction(image_input, feature_extractor_4, model_4) |
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viz_img_1 = visualize_prediction(image_input, processed_outputs_1, threshold, model_1.config.id2label) |
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viz_img_2 = visualize_prediction(image_input, processed_outputs_2, threshold, model_2.config.id2label) |
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viz_img_3 = visualize_prediction(image_input, processed_outputs_3, threshold, model_3.config.id2label) |
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viz_img_4 = visualize_prediction(image_input, processed_outputs_4, threshold, model_4.config.id2label) |
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return viz_img_1,viz_img_2,viz_img_3,viz_img_4 |
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title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>""" |
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css = ''' |
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h1#title { |
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text-align: center; |
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} |
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''' |
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demo = gr.Blocks(css=css) |
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with demo: |
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gr.Markdown(title) |
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options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True) |
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slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold') |
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with gr.Tabs(): |
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with gr.TabItem('Image URL'): |
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with gr.Row(): |
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url_input = gr.Textbox(lines=2,label='Enter valid image URL here..') |
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img_output_from_url = gr.Image(shape=(650,650)) |
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url_but = gr.Button('Detect') |
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with gr.TabItem('Image Upload'): |
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with gr.Row(): |
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img_input = gr.Image(type='pil') |
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img_output_from_upload= gr.Image(shape=(650,650)) |
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with gr.Row(): |
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example_images = gr.Dataset(components=[img_input], |
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samples=[[path.as_posix()] |
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for path in sorted(pathlib.Path('images').rglob('*.JPG'))]) |
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img_but = gr.Button('Detect') |
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img_but.click(detect_objects,inputs=[img_input,slider_input],outputs=img_output_from_upload,queue=True) |
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demo.launch(enable_queue=True) |
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