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import gradio as gr
from PIL import Image, ImageDraw, ImageFont
# Use a pipeline as a high-level helper
from transformers import pipeline
object_detection = pipeline(
"object-detection",
model="facebook/detr-resnet-50")
def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
"""
Draws bounding boxes on the given image based on the detections.
:param image: PIL.Image object
:param detections: List of detection results, where each result is a dictionary containing
'score', 'label', and 'box' keys. 'box' itself is a dictionary with 'xmin',
'ymin', 'xmax', 'ymax'.
:param font_path: Path to the TrueType font file to use for text.
:param font_size: Size of the font to use for text.
:return: PIL.Image object with bounding boxes drawn.
"""
# Make a copy of the image to draw on
draw_image = image.copy()
draw = ImageDraw.Draw(draw_image)
# Load custom font or default font if path not provided
if font_path:
font = ImageFont.truetype(font_path, font_size)
else:
# When font_path is not provided, load default font but it's size is fixed
font = ImageFont.load_default()
# Increase font size workaround by using a TTF font file, if needed, can download and specify the path
for detection in detections:
box = detection['box']
xmin = box['xmin']
ymin = box['ymin']
xmax = box['xmax']
ymax = box['ymax']
# Draw the bounding box
draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
# Optionally, you can also draw the label and score
label = detection['label']
score = detection['score']
text = f"{label} {score:.2f}"
# Draw text with background rectangle for visibility
if font_path: # Use the custom font with increased size
text_size = draw.textbbox((xmin, ymin), text, font=font)
else:
# Calculate text size using the default font
text_size = draw.textbbox((xmin, ymin), text)
draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
draw.text((xmin, ymin), text, fill="white", font=font)
return draw_image
def detect_object(image):
raw_image = image
output = object_detection(raw_image)
processed_image = draw_bounding_boxes(raw_image, output)
return processed_image
demo = gr.Interface(fn=detect_object,
inputs=[gr.Image(label="Select Image",type="pil")],
outputs=[gr.Image(label="Processed Image", type="pil")],
title="@caesar-2series: Image Object Detection",
description="Find Items in the Given Input Image")
demo.launch() |