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import gradio as gr
import torch
from torchvision import models, transforms
from PIL import Image, ImageEnhance
import numpy as np
import cv2
model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()
transform = transforms.Compose([
transforms.ToTensor()
])
def detect_dress(image):
image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
outputs = model(image_tensor)
boxes = outputs[0]['boxes'].numpy()
scores = outputs[0]['scores'].numpy()
threshold = 0.8
dress_boxes = [box for box, score in zip(boxes, scores) if score > threshold]
draw = ImageDraw.Draw(image)
for box in dress_boxes:
draw.rectangle(box.tolist(), outline="red", width=3)
return image, dress_boxes
def crop_image(image, box):
return image.crop(box)
def adjust_color(image, factor):
enhancer = ImageEnhance.Color(image)
return enhancer.enhance(factor)
def process_image(image, edit_type, factor):
detected_image, boxes = detect_dress(image)
if not boxes:
return detected_image, "No dresses detected."
if edit_type == "Crop":
box = boxes[0]
edited_image = crop_image(image, box)
elif edit_type == "Adjust Color":
edited_image = adjust_color(image, factor)
else:
edited_image = image
return edited_image, "Edit applied."
iface = gr.Interface(
fn=process_image,
inputs=[
gr.inputs.Image(type="pil"),
gr.inputs.Radio(choices=["None", "Crop", "Adjust Color"]),
gr.inputs.Slider(0.5, 2.0, step=0.1, label="Factor")
],
outputs=[
gr.outputs.Image(type="pil"),
gr.outputs.Textbox(label="Result")
],
live=True
)
iface.launch() |