test

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example/human/human01.jpg filter=lfs diff=lfs merge=lfs -text
+example/human/human02.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,14 @@
 ---
-title: Luxtryon
-emoji: 📊
-colorFrom: indigo
-colorTo: yellow
+title: Fashion Virtual Tryon
+emoji: 😻
+colorFrom: blue
+colorTo: gray
 sdk: gradio
-sdk_version: 5.31.0
+sdk_version: 5.23.3
 app_file: app.py
 pinned: false
-license: other
+license: mit
+short_description: Fashion Virtual Try-on
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,279 @@
+import os
+import cv2
+import gradio as gr
+import mediapipe as mp
+import numpy as np
+from PIL import Image
+from gradio_client import Client, handle_file
+
+# Set up paths
+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 align_clothing(body_img, clothing_img):
+    image_rgb = cv2.cvtColor(body_img, cv2.COLOR_BGR2RGB)
+    result = pose.process(image_rgb)
+    output = body_img.copy()
+
+    if result.pose_landmarks:
+        h, w, _ = output.shape
+
+        # Extract key points
+        def get_point(landmark_id):
+            lm = result.pose_landmarks.landmark[landmark_id]
+            return int(lm.x * w), int(lm.y * h)
+
+        left_shoulder = get_point(mp_pose_landmark.LEFT_SHOULDER)
+        right_shoulder = get_point(mp_pose_landmark.RIGHT_SHOULDER)
+        left_hip = get_point(mp_pose_landmark.LEFT_HIP)
+        right_hip = get_point(mp_pose_landmark.RIGHT_HIP)
+
+        # Destination box (torso region)
+        dst_pts = np.array([
+            left_shoulder,
+            right_shoulder,
+            right_hip,
+            left_hip
+        ], dtype=np.float32)
+
+        # Source box (clothing image corners)
+        src_h, src_w = clothing_img.shape[:2]
+        src_pts = np.array([
+            [0, 0],
+            [src_w, 0],
+            [src_w, src_h],
+            [0, src_h]
+        ], dtype=np.float32)
+
+        # Compute perspective transform and warp
+        matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)
+        warped_clothing = cv2.warpPerspective(clothing_img, matrix, (w, h), borderMode=cv2.BORDER_TRANSPARENT)
+
+        # Handle transparency
+        if clothing_img.shape[2] == 4:
+            alpha = warped_clothing[:, :, 3] / 255.0
+            for c in range(3):
+                output[:, :, c] = (1 - alpha) * output[:, :, c] + alpha * warped_clothing[:, :, c]
+        else:
+            output = cv2.addWeighted(output, 0.8, warped_clothing, 0.5, 0)
+
+    return output
+
+def process_image(human_img_path, garm_img_path):
+    client = Client("franciszzj/Leffa")
+
+    result = client.predict(
+        src_image_path=handle_file(human_img_path),
+        ref_image_path=handle_file(garm_img_path),
+        ref_acceleration=False,
+        step=30,
+        scale=2.5,
+        seed=42,
+        vt_model_type="viton_hd",
+        vt_garment_type="upper_body",
+        vt_repaint=False,
+        api_name="/leffa_predict_vt"
+    )
+
+    print(result)
+    generated_image_path = result[0]
+    print("generated_image_path" + generated_image_path)
+    generated_image = Image.open(generated_image_path)
+
+    return generated_image
+
+# Custom CSS for better styling
+custom_css = """
+.gradio-container {
+    max-width: 1200px !important;
+}
+.container {
+    max-width: 1200px;
+    margin: auto;
+    padding: 20px;
+}
+.header {
+    text-align: center;
+    margin-bottom: 30px;
+}
+.header h1 {
+    font-size: 2.5rem;
+    margin-bottom: 10px;
+    background: linear-gradient(45deg, #FF6B6B, #4ECDC4);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+.header p {
+    font-size: 1.1rem;
+    color: #666;
+}
+.image-container {
+    border-radius: 10px;
+    overflow: hidden;
+    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+}
+.upload-section {
+    background: #f9f9f9;
+    padding: 20px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+.try-btn {
+    background: linear-gradient(45deg, #FF6B6B, #4ECDC4) !important;
+    color: white !important;
+    font-weight: bold !important;
+    padding: 12px 24px !important;
+    border-radius: 50px !important;
+    border: none !important;
+}
+.try-btn:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 6px 12px rgba(0,0,0,0.15);
+}
+.examples-section {
+    margin-top: 15px;
+}
+.examples-section h3 {
+    margin-bottom: 10px;
+    color: #555;
+}
+"""
+
+with gr.Blocks(css=custom_css, title="Virtual Try-On Fashion") as demo:
+    with gr.Column(elem_classes=["container"]):
+        with gr.Column(elem_classes=["header"]):
+            gr.HTML("""
+            <h1>Virtual Try-On Fashion</h1>
+            <p>Upload your photo and select a garment to see how it looks on you! ✨</p>
+            """)
+        
+        with gr.Row():
+            with gr.Column(elem_classes=["upload-section"]):
+                gr.Markdown("### Step 1: Upload Your Photo")
+                human_img = gr.Image(
+                    type="filepath", 
+                    label='Person Image',
+                    interactive=True,
+                    elem_classes=["image-container"]
+                )
+                with gr.Column(elem_classes=["examples-section"]):
+                    gr.Markdown("**Example poses:**")
+                    example = gr.Examples(
+                        inputs=human_img,
+                        examples_per_page=5,
+                        examples=human_list_path,
+                        label=None
+                    )
+            
+            with gr.Column(elem_classes=["upload-section"]):
+                gr.Markdown("### Step 2: Select Garment")
+                garm_img = gr.Image(
+                    label="Clothing Item", 
+                    type="filepath", 
+                    interactive=True,
+                    elem_classes=["image-container"]
+                )
+                with gr.Column(elem_classes=["examples-section"]):
+                    gr.Markdown("**Example garments:**")
+                    example = gr.Examples(
+                        inputs=garm_img,
+                        examples_per_page=5,
+                        examples=garm_list_path,
+                        label=None
+                    )
+            
+            with gr.Column():
+                gr.Markdown("### Step 3: See the Result")
+                image_out = gr.Image(
+                    label="Virtual Try-On Result", 
+                    type="pil",
+                    elem_classes=["image-container"],
+                    interactive=False
+                )
+                with gr.Row():
+                    gr.ClearButton([human_img, garm_img, image_out])
+        
+        with gr.Row():
+            try_button = gr.Button(
+                value="Try It On Now", 
+                variant='primary',
+                elem_classes=["try-btn"]
+            )
+        
+        # Add some information sections
+        with gr.Accordion("ℹ️ How to use this tool", open=False):
+            gr.Markdown("""
+            1. **Upload your photo**: Choose a clear front-facing photo with visible shoulders and hips
+            2. **Select a garment**: Pick from our examples or upload your own clothing image
+            3. **Click 'Try It On Now'**: See how the clothing looks on you instantly!
+            
+            For best results:
+            - Use well-lit photos with good contrast
+            - Avoid baggy clothing in your reference photo
+            - Front-facing poses work best
+            """)
+        
+        with gr.Accordion("⚠️ Limitations", open=False):
+            gr.Markdown("""
+            - Works best with upper body garments (shirts, jackets)
+            - May not work perfectly with complex patterns or textures
+            - Results depend on pose detection accuracy
+            - Currently optimized for front-facing poses
+            """)
+
+    # Linking the button to the processing function
+    try_button.click(
+        fn=process_image, 
+        inputs=[human_img, garm_img], 
+        outputs=image_out,
+        api_name="try_on"
+    )
+
+if __name__ == "__main__":
+    demo.launch(show_error=True, share=False)

requirements.txt

@@ -0,0 +1,7 @@
+mediapipe>=0.10.8
+numpy==1.26.4
+opencv-contrib-python==4.11.0.86
+opencv-python==4.11.0.86
+gradio==5.23.3
+gradio_client==1.8.0
+