Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import torch
+import cv2
+from PIL import Image
+from torchvision import transforms
+from cloth_segmentation.networks.u2net import U2NET
+
+# ---------------------- MODEL LOAD ---------------------- #
+@st.cache_resource
+def load_model():
+    model_path = "cloth_segmentation/networks/u2net.pth"
+    model = U2NET(3, 1)
+    state_dict = torch.load(model_path, map_location=torch.device('cpu'))
+    state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+    model.load_state_dict(state_dict)
+    model.eval()
+    return model
+
+model = load_model()
+
+# ---------------------- UTILITY FUNCTIONS ---------------------- #
+def refine_mask(mask):
+    close_kernel = np.ones((5, 5), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel)
+    erode_kernel = np.ones((3, 3), np.uint8)
+    mask = cv2.erode(mask, erode_kernel, iterations=1)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel)
+    mask = cv2.GaussianBlur(mask, (5, 5), 1.5)
+    return mask
+
+def segment_dress(image_np):
+    transform_pipeline = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Resize((320, 320))
+    ])
+    image = Image.fromarray(image_np).convert("RGB")
+    input_tensor = transform_pipeline(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(input_tensor)[0][0].squeeze().cpu().numpy()
+    output = (output - output.min()) / (output.max() - output.min() + 1e-8)
+    adaptive_thresh = np.mean(output) + 0.2
+    dress_mask = (output > adaptive_thresh).astype(np.uint8) * 255
+    dress_mask = cv2.resize(dress_mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST)
+    return refine_mask(dress_mask)
+
+def apply_grabcut(image_np, dress_mask):
+    bgd_model = np.zeros((1, 65), np.float64)
+    fgd_model = np.zeros((1, 65), np.float64)
+    mask = np.where(dress_mask > 0, cv2.GC_PR_FGD, cv2.GC_BGD).astype('uint8')
+    coords = cv2.findNonZero(dress_mask)
+    if coords is not None:
+        x, y, w, h = cv2.boundingRect(coords)
+        rect = (x, y, w, h)
+        cv2.grabCut(image_np, mask, rect, bgd_model, fgd_model, 3, cv2.GC_INIT_WITH_MASK)
+    refined_mask = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 255, 0).astype("uint8")
+    return refine_mask(refined_mask)
+
+def recolor_dress(image_np, dress_mask, target_color):
+    target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]
+    img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
+    dress_pixels = img_lab[dress_mask > 0]
+    if len(dress_pixels) == 0:
+        return image_np
+    mean_L, mean_A, mean_B = np.mean(dress_pixels, axis=0)
+    a_shift = target_color_lab[1] - mean_A
+    b_shift = target_color_lab[2] - mean_B
+    img_lab[..., 1] = np.clip(img_lab[..., 1] + (dress_mask / 255.0) * a_shift, 0, 255)
+    img_lab[..., 2] = np.clip(img_lab[..., 2] + (dress_mask / 255.0) * b_shift, 0, 255)
+    img_recolored = cv2.cvtColor(img_lab.astype(np.uint8), cv2.COLOR_LAB2RGB)
+    feathered_mask = cv2.GaussianBlur(dress_mask, (21, 21), 7)
+    lightness_mask = (img_lab[..., 0] / 255.0) ** 0.7
+    adaptive_feather = (feathered_mask * lightness_mask).astype(np.uint8)
+    return (image_np * (1 - adaptive_feather[..., None] / 255) + img_recolored * (adaptive_feather[..., None] / 255)).astype(np.uint8)
+
+def change_dress_color(img, color):
+    color_map = {
+        "Red": (0, 0, 255), "Blue": (255, 0, 0), "Green": (0, 255, 0),
+        "Yellow": (0, 255, 255), "Purple": (128, 0, 128), "Orange": (0, 165, 255),
+        "Cyan": (255, 255, 0), "Magenta": (255, 0, 255), "White": (255, 255, 255),
+        "Black": (0, 0, 0)
+    }
+    new_color_bgr = color_map.get(color, (0, 0, 255))
+    img_np = np.array(img)
+    try:
+        dress_mask = segment_dress(img_np)
+        if np.sum(dress_mask) < 1000:
+            return img
+        dress_mask = apply_grabcut(img_np, dress_mask)
+        img_recolored = recolor_dress(img_np, dress_mask, new_color_bgr)
+        return Image.fromarray(img_recolored)
+    except Exception as e:
+        st.error(f"Error processing image: {str(e)}")
+        return img
+
+# ---------------------- STREAMLIT UI ---------------------- #
+st.title("👗 AI Dress Color Changer")
+st.markdown("Upload a dress image and select a new color for realistic recoloring")
+
+uploaded_img = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+color_option = st.selectbox("Choose a Color", [
+    "Red", "Blue", "Green", "Yellow", "Purple", 
+    "Orange", "Cyan", "Magenta", "White", "Black"
+])
+
+if uploaded_img:
+    image = Image.open(uploaded_img).convert("RGB")
+    st.image(image, caption="Original Image", use_column_width=True)
+
+    if st.button("Recolor Dress"):
+        with st.spinner("Processing..."):
+            result = change_dress_color(image, color_option)
+            st.image(result, caption="Recolored Dress", use_column_width=True)