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a2bdad7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
import requests
import random
from IPython.display import display
import gradio as gr
# you can specify the revision tag if you don't want the timm dependency
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
def draw_detections(image, outputs, processor, model, threshold=0.9):
"""
Draw bounding boxes and labels on an image using detection results.
Args:
image (PIL.Image): Input image.
outputs (dict): Model output.
processor: The processor used for post-processing.
model: The object detection model.
threshold (float): Confidence threshold.
Returns:
PIL.Image: The image with bounding boxes drawn.
"""
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(
outputs, target_sizes=target_sizes, threshold=threshold
)[0]
draw_image = image.copy()
draw = ImageDraw.Draw(draw_image, "RGBA")
# define fixed colors per label for consistency
COLORS = {}
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
label_name = model.config.id2label[label.item()]
# assign consistent random color for each label type
if label_name not in COLORS:
COLORS[label_name] = tuple(random.choices(range(256), k=3))
color = COLORS[label_name]
# draw translucent box
draw.rectangle(box, fill=color + (80,), outline=color, width=3)
draw.text((box[0] + 3, box[1] + 3),
f"{label_name} {round(score.item(), 2)}",
fill=(255, 255, 255, 255))
return draw_image
def detect_and_draw(img):
inputs = processor(images=img, return_tensors="pt")
outputs = model(**inputs)
return draw_detections(img, outputs, processor, model)
demo = gr.Interface(
fn=detect_and_draw,
inputs=gr.Image(type="pil"),
outputs="image",
title="Object Detection Viewer"
)
demo.launch(show_error=True) |