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| import torch | |
| from transformers import AutoImageProcessor, AutoModelForObjectDetection | |
| #from transformers import pipeline | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as patches | |
| import io | |
| from random import choice | |
| image_processor_tiny = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny") | |
| model_tiny = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny") | |
| import gradio as gr | |
| COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff", | |
| "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf", | |
| "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"] | |
| fdic = { | |
| "family" : "DejaVu Serif", | |
| "style" : "normal", | |
| "size" : 18, | |
| "color" : "yellow", | |
| "weight" : "bold" | |
| } | |
| def get_figure(in_pil_img, in_results): | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(in_pil_img) | |
| ax = plt.gca() | |
| for score, label, box in zip(in_results["scores"], in_results["labels"], in_results["boxes"]): | |
| selected_color = choice(COLORS) | |
| box_int = [i.item() for i in torch.round(box).to(torch.int32)] | |
| x, y, w, h = box_int[0], box_int[1], box_int[2]-box_int[0], box_int[3]-box_int[1] | |
| #x, y, w, h = torch.round(box[0]).item(), torch.round(box[1]).item(), torch.round(box[2]-box[0]).item(), torch.round(box[3]-box[1]).item() | |
| ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3, alpha=0.8)) | |
| ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 2)}%", fontdict=fdic, alpha=0.8) | |
| plt.axis("off") | |
| return plt.gcf() | |
| def infer(in_pil_img, in_threshold=0.9): | |
| target_sizes = torch.tensor([in_pil_img.size[::-1]]) | |
| inputs = image_processor_tiny(images=in_pil_img, return_tensors="pt") | |
| outputs = model_tiny(**inputs) | |
| # convert outputs (bounding boxes and class logits) to COCO API | |
| results = image_processor_tiny.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[0] | |
| figure = get_figure(in_pil_img, results) | |
| buf = io.BytesIO() | |
| figure.savefig(buf, bbox_inches='tight') | |
| buf.seek(0) | |
| output_pil_img = Image.open(buf) | |
| return output_pil_img | |
| with gr.Blocks(title="Object Detection") as demo: | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input image", type="pil") | |
| output_image = gr.Image(label="Output image with predicted instances", type="pil") | |
| gr.Examples(['samples/1.jpeg', 'samples/2.JPG'], inputs=input_image) | |
| threshold = gr.Slider(0, 1.0, value=0.9, label='threshold') | |
| send_btn = gr.Button("Infer") | |
| send_btn.click(fn=infer, inputs=[input_image, threshold], outputs=[output_image]) | |
| #demo.queue() | |
| demo.launch(debug=True) |