Create app.py

app.py

@@ -0,0 +1,142 @@
+import gradio as gr
+from transformers import pipeline, AutoImageProcessor, MobileNetV2ForSemanticSegmentation
+from PIL import Image
+import numpy as np
+import cv2
+
+# Load segmentation pipeline and model
+pipe = pipeline("image-segmentation", model="google/deeplabv3_mobilenet_v2_1.0_513")
+processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+
+def VirtualTryOn(user_image, clothing_image):
+    if isinstance(user_image, np.ndarray):
+        user_image = Image.fromarray(user_image)
+
+    if isinstance(clothing_image, np.ndarray):
+        clothing_image = Image.fromarray(clothing_image)
+
+    if isinstance(user_image, Image.Image) and isinstance(clothing_image, Image.Image):
+        user_segmentation = pipe(user_image)
+        user_mask = user_segmentation[0]['mask']
+
+        clothing_segmentation = pipe(clothing_image)
+        clothing_mask = clothing_segmentation[0]['mask']
+
+        user_mask_array = np.array(user_mask)
+        clothing_mask_array = np.array(clothing_mask)
+
+        user_image_array = np.array(user_image)
+        clothing_image_array = np.array(clothing_image)
+
+        user_isolated = cv2.bitwise_and(user_image_array, user_image_array, mask=user_mask_array)
+        clothing_isolated = cv2.bitwise_and(clothing_image_array, clothing_image_array, mask=clothing_mask_array)
+
+        user_height, user_width, _ = user_isolated.shape
+        clothing_resized = cv2.resize(clothing_isolated, (user_width, user_height))
+
+        combined_image = cv2.add(user_isolated, clothing_resized)
+        combined_image = Image.fromarray(combined_image)
+
+        return combined_image
+    else:
+        raise ValueError("Both inputs should be PIL images")
+
+css = """
+.gradio-container {
+    background-color: #f5f5f5;
+    font-family: 'Arial', sans-serif;
+    padding: 20px;
+    border-radius: 15px;
+    box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1);
+    width: 90vw;
+    max-width: 1200px;
+    margin: auto;
+}
+h1 {
+    color: #333333;
+    text-align: center;
+    font-size: 2.5rem;
+    margin-bottom: 20px;
+}
+#images-container {
+    display: flex;
+    justify-content: space-around;
+    align-items: center;
+    gap: 20px;
+    padding: 15px;
+    border: 2px solid #cccccc;
+    border-radius: 15px;
+    background-color: #ffffff;
+    box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
+}
+.image-container {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 10px;
+}
+.image-container label {
+    font-weight: bold;
+    color: #555555;
+}
+.image-box {
+    width: 220px;
+    height: 300px;
+    border: 3px dashed #aaaaaa;
+    border-radius: 10px;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    background-color: #f9f9f9;
+}
+button {
+    font-size: 1.2rem;
+    padding: 10px 20px;
+    border-radius: 10px;
+    border: none;
+    cursor: pointer;
+    transition: all 0.3s ease;
+}
+#try-on-button {
+    background-color: #4CAF50;
+    color: white;
+}
+#try-on-button:hover {
+    background-color: #45a049;
+}
+#clear-button {
+    background-color: #FF5722;
+    color: white;
+}
+#clear-button:hover {
+    background-color: #e64a19;
+}
+"""
+
+with gr.Blocks(css=css) as iface:
+    gr.Markdown("<h1>Virtual Try-On Application</h1>")
+
+    with gr.Row(elem_id="images-container"):
+        with gr.Column(elem_id="user-image-container", elem_classes="image-container"):
+            gr.Markdown("**Upload Person Image**")
+            user_image = gr.Image(type="pil", label="Person Image", elem_id="user-image", elem_classes="image-box")
+
+        with gr.Column(elem_id="clothing-image-container", elem_classes="image-container"):
+            gr.Markdown("**Upload Clothing Image**")
+            clothing_image = gr.Image(type="pil", label="Clothing Image", elem_id="clothing-image", elem_classes="image-box")
+
+        with gr.Column(elem_id="output-image-container", elem_classes="image-container"):
+            gr.Markdown("**Try-On Result**")
+            output = gr.Image(type="pil", label="Result", elem_id="output", elem_classes="image-box")
+
+    with gr.Row():
+        with gr.Column():
+            try_on_button = gr.Button("Try On", elem_id="try-on-button")
+        with gr.Column():
+            clear_button = gr.Button("Clear", elem_id="clear-button")
+
+    try_on_button.click(fn=VirtualTryOn, inputs=[user_image, clothing_image], outputs=output)
+    clear_button.click(fn=lambda: (None, None, None), inputs=[], outputs=[user_image, clothing_image, output])
+
+iface.launch()