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| import io | |
| import matplotlib.pyplot as plt | |
| import requests | |
| import streamlit as st | |
| import torch | |
| from PIL import Image | |
| from transformers import DetrFeatureExtractor, DetrForObjectDetection | |
| # 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], | |
| ] | |
| def get_hf_components(model_name_or_path): | |
| feature_extractor = DetrFeatureExtractor.from_pretrained(model_name_or_path) | |
| model = DetrForObjectDetection.from_pretrained(model_name_or_path) | |
| model.eval() | |
| return feature_extractor, model | |
| def get_img_from_url(url): | |
| return Image.open(requests.get(url, stream=True).raw) | |
| 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 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 main(): | |
| option = st.selectbox("Which model should we use?", ("facebook/detr-resnet-50", "facebook/detr-resnet-101")) | |
| feature_extractor, model = get_hf_components(option) | |
| url = st.text_input("URL to some image", "http://images.cocodataset.org/val2017/000000039769.jpg") | |
| img = get_img_from_url(url) | |
| processed_outputs = make_prediction(img, feature_extractor, model) | |
| threshold = st.slider("Prediction Threshold", 0.0, 1.0, 0.7) | |
| viz_img = visualize_prediction(img, processed_outputs, threshold, model.config.id2label) | |
| st.image(viz_img) | |
| if __name__ == '__main__': | |
| main() | |