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import gradio as gr
from transformers import AutoImageProcessor, AutoModelForObjectDetection
import torch
from PIL import Image, ImageDraw
# Load the model and processor
processor = AutoImageProcessor.from_pretrained("0llheaven/CON-DETR-V5")
model = AutoModelForObjectDetection.from_pretrained("0llheaven/CON-DETR-V5")
def detect_objects(image, score_threshold):
# Convert image to RGB if it's grayscale
if image.mode != "RGB":
image = image.convert("RGB")
# Prepare input for the model
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# Filter predictions based on the user-defined score threshold
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)
labels_output = []
no_detection = True
# Draw bounding boxes around detected objects
draw = ImageDraw.Draw(image)
for result in results:
scores = result["scores"]
labels = result["labels"]
boxes = result["boxes"]
for score, label, box in zip(scores, labels, boxes):
if score >= score_threshold: # Only draw if score is above threshold
no_detection = False
box = [round(i, 2) for i in box.tolist()]
#label_name = "Pneumonia" if label.item() == 0 else "Other"
if label.item() == 0:
label_name = "Pneumonia"
elif label.item() == 1:
label_name = "Normal"
elif label.item() == 2:
label_name = "Pneumonia_bacteria"
else:
label_name = "Pneumonia_virus"
draw.rectangle(box, outline="red", width=3)
draw.text((box[0], box[1]), f"{label_name}: {round(score.item(), 3)}", fill="red")
labels_output.append(f"{label_name}: {round(score.item(), 3)}")
# If no detections, set label as 'Other'
if no_detection:
labels_output.append("Other")
return image, "\n".join(labels_output)
# Create the Gradio interface
interface = gr.Interface(
fn=detect_objects,
inputs=[gr.Image(type="pil"), gr.Slider(0, 1, value=0.5, label="Score Threshold")], # Add slider for score threshold
# outputs=gr.Image(type="pil"), # Corrected output type
outputs=[gr.Image(type="pil"), gr.Textbox(label="Detected Objects")],
title="Object Detection with Transformers",
description="Upload an image to detect objects using a fine-tuned Conditional-DETR model."
)
# Launch the interface
interface.launch() |