from transformers import AutoFeatureExtractor, AutoModelForObjectDetection import matplotlib.pyplot as plt import matplotlib.patches as patches from random import choice from PIL import Image import os from matplotlib import rcParams, font_manager extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50") model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50") from transformers import pipeline pipe = pipeline('object-detection', model=model, feature_extractor=extractor) img_url = st.text_input('Image URL', 'https://images.unsplash.com/photo-1556911220-bff31c812dba?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=2468&q=80') output = pipe(img_url) fpath = os.path.join(r"Poppins-SemiBold.ttf") prop = font_manager.FontProperties(fname=fpath) img = Image.open('kitchen.jpg') plt.figure(dpi=2400) # Create figure and axes fig, ax = plt.subplots() # Display the image ax.imshow(img) colors = ["#ef4444", "#f97316", "#eab308", "#84cc16", "#06b6d4", "#6366f1"] # Create a Rectangle patch for prediction in output: selected_color = choice(colors) x, y, w, h = prediction['box']['xmin'], prediction['box']['ymin'], prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin'] rect = patches.FancyBboxPatch((x, y), w, h, linewidth=1.25, edgecolor=selected_color, facecolor='none', boxstyle="round,pad=-0.0040,rounding_size=10",) ax.add_patch(rect) plt.text(x, y-25, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontsize=100, color=selected_color, fontproperties=prop) plt.axis('off') plt.savefig('kitchen-bbox.jpg', dpi=1200, bbox_inches='tight') image = Image.open('kitchen-bbox.jpg') st.image(image, caption='DETR Image') plt.show()