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import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
import os
example_path = os.path.join(os.path.dirname(__file__), 'example')
garm_list = os.listdir(os.path.join(example_path, "cloth"))
garm_list_path = [os.path.join(example_path, "cloth", garm) for garm in garm_list]
human_list = os.listdir(os.path.join(example_path, "human"))
human_list_path = [os.path.join(example_path, "human", human) for human in human_list]
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=True)
mp_drawing = mp.solutions.drawing_utils
mp_pose_landmark = mp_pose.PoseLandmark
def detect_pose(image):
# Convert to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Run pose detection
result = pose.process(image_rgb)
keypoints = {}
if result.pose_landmarks:
# Draw landmarks on image
mp_drawing.draw_landmarks(image, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)
# Get image dimensions
height, width, _ = image.shape
# Extract specific landmarks
landmark_indices = {
'left_shoulder': mp_pose_landmark.LEFT_SHOULDER,
'right_shoulder': mp_pose_landmark.RIGHT_SHOULDER,
'left_hip': mp_pose_landmark.LEFT_HIP,
'right_hip': mp_pose_landmark.RIGHT_HIP
}
for name, index in landmark_indices.items():
lm = result.pose_landmarks.landmark[index]
x, y = int(lm.x * width), int(lm.y * height)
keypoints[name] = (x, y)
# Draw a circle + label for debug
cv2.circle(image, (x, y), 5, (0, 255, 0), -1)
cv2.putText(image, name, (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return image
def process_image(human_img):
# Convert PIL image to NumPy array
human_img = np.array(human_img)
processed_image = detect_pose(human_img)
return processed_image
image_blocks = gr.Blocks().queue()
with image_blocks as demo:
gr.HTML("<center><h1>Virtual Try-On</h1></center>")
gr.HTML("<center><p>Upload an image of a person and an image of a garment ✨</p></center>")
with gr.Row():
with gr.Column():
human_img = gr.Image(type="pil", label='Human', interactive=True)
example = gr.Examples(
inputs=human_img,
examples_per_page=10,
examples=human_list_path
)
with gr.Column():
garm_img = gr.Image(label="Garment", type="pil", interactive=True)
example = gr.Examples(
inputs=garm_img,
examples_per_page=8,
examples=garm_list_path)
with gr.Column():
image_out = gr.Image(label="Processed image", type="pil")
with gr.Row():
try_button = gr.Button(value="Try-on", variant='primary')
# Linking the button to the processing function
try_button.click(fn=process_image, inputs=human_img, outputs=image_out)
image_blocks.launch()
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