| import gradio as gr |
| from PIL import Image, ImageDraw |
| from transformers import pipeline |
| import numpy as np |
|
|
|
|
| def plot_results(image, results, threshold=0.7): |
| image = Image.fromarray(np.uint8(image)) |
| draw = ImageDraw.Draw(image) |
| for result in results: |
| score = result["score"] |
| label = result["label"] |
| box = list(result["box"].values()) |
| if score > threshold: |
| x, y, x2, y2 = tuple(box) |
| draw.rectangle((x, y, x2, y2), outline="red", width=1) |
| draw.text((x, y), label, fill="white") |
| draw.text( |
| (x + 0.5, y - 0.5), |
| text=str(score), |
| fill="green" if score > 0.7 else "red", |
| ) |
| return image |
|
|
| def predict(image): |
| |
| obj_detector = pipeline( |
| "object-detection", model="Antoine101/detr-resnet-50-dc5-fashionpedia-finetuned" |
| ) |
| results = obj_detector(image) |
| print(results) |
| return plot_results(image, results) |
|
|
| title = "Are you fashion?" |
| description = """ |
| DETR model finetuned on "detection-datasets/fashionpedia" for apparels detection. |
| """ |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=gr.Image(label="Input Image", type="pil"), |
| outputs="image", |
| examples=[["example1.jpg"]], |
| title=title, |
| description=description |
| ) |
| demo.launch() |
|
|
