app.py

import gradio as gr
import pandas as pd
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image, ImageDraw
import torch
from transformers import DetrImageProcessor, DetrForObjectDetection


#image_processor = AutoImageProcessor.from_pretrained('hustvl/yolos-small')
#model = AutoModelForObjectDetection.from_pretrained('hustvl/yolos-small')
image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")

colors = ["red",
          "orange",
          "yellow",
          "green",
          "blue",
          "indigo",
          "violet",
          "brown",
          "black",
          "slategray",
         ]

# Resized image width
WIDTH = 900

def detect(image):
    print(image)
    width, height = image.size
    ratio = float(WIDTH) / float(width)
    new_h = height * ratio

    image = image.resize((int(WIDTH), int(new_h)), Image.Resampling.LANCZOS)
    
    inputs = image_processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # convert outputs to COCO API
    target_sizes = torch.tensor([image.size[::-1]])
    results = image_processor.post_process_object_detection(outputs,threshold=0.9, target_sizes=target_sizes)[0]

    draw = ImageDraw.Draw(image)
    
    # label and the count
    counts = {}

    for score, label in zip(results["scores"], results["labels"]):
        label_name = model.config.id2label[label.item()]
        if label_name not in counts:
            counts[label_name] = 0
        counts[label_name] += 1

    count_results = {k: v for k, v in (sorted(counts.items(), key=lambda item: item[1], reverse=True)[:10])}
    label2color = {}
    for idx, label in enumerate(count_results):
        label2color[label] = colors[idx]

    for label, box in zip(results["labels"], results["boxes"]):
        label_name = model.config.id2label[label.item()]

        if label_name in count_results:
            box = [round(i, 4) for i in box.tolist()]
            x1, y1, x2, y2 = tuple(box)
            draw.rectangle((x1, y1, x2, y2), outline=label2color[label_name], width=2)
            draw.text((x1, y1), label_name, fill="white")

    df = pd.DataFrame({
        'label': [label for label in count_results],
        'counts': [counts[label] for label in count_results]
    })
    
    return image, df, count_results

demo = gr.Interface(
    fn=detect,
    inputs=[gr.Image(label="Input image", type="pil")],
    outputs=[gr.Image(label="Output image"), gr.BarPlot(show_label=False, x="label", y="counts", x_title="Labels", y_title="Counts", vertical=False), gr.Textbox(show_label=False)],
    title="FB Object Detection",
    cache_examples=False
)

demo.launch()