Update app.py

app.py

@@ -7,111 +7,54 @@ import numpy as np
 from huggingface_hub import hf_hub_download
 import gradio as gr
 
-class PortraitGenerator:
-    def __init__(self):
-        self.u2net = self.load_u2net_model()
-
-    def normPRED(self, d):
-        return (d - torch.min(d)) / (torch.max(d) - torch.min(d))
-
-    def inference(self, input_img):
-        input_img = input_img / np.max(input_img)
-        tmpImg = np.zeros((input_img.shape[0], input_img.shape[1], 3))
-        tmpImg[:, :, 0] = (input_img[:, :, 2] - 0.406) / 0.225
-        tmpImg[:, :, 1] = (input_img[:, :, 1] - 0.456) / 0.224
-        tmpImg[:, :, 2] = (input_img[:, :, 0] - 0.485) / 0.229
-        tmpImg = torch.from_numpy(tmpImg.transpose((2, 0, 1))[np.newaxis, :, :, :]).type(torch.FloatTensor)
-        tmpImg = Variable(tmpImg.cuda() if torch.cuda.is_available() else tmpImg)
-        d1, _, _, _, _, _, _ = self.u2net(tmpImg)
-        pred = self.normPRED(1.0 - d1[:, 0, :, :])
-        return pred.cpu().data.numpy().squeeze()
-
-    def adjust_image(self, img, apply_bw, brightness, contrast, saturation, white_balance, hue, highlights_shadows, sharpness, noise_reduction):
-        # Convert to grayscale if needed
-        if apply_bw:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-        # Adjust brightness and contrast
-        img = cv2.convertScaleAbs(img, alpha=contrast / 50.0, beta=brightness - 50)
-
-        # Adjust saturation
-        if saturation != 50:
-            hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            hsv_img[:, :, 1] = np.clip(hsv_img[:, :, 1] * (saturation / 50.0), 0, 255)
-            img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
-
-        # Adjust white balance
-        if white_balance != 50:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
-            l, a, b = cv2.split(img)
-            a = a * (white_balance / 50.0)
-            b = b * (white_balance / 50.0)
-            img = cv2.merge((l, a, b))
-            img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)
-
-        # Adjust hue
-        if hue != 50:
-            hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            hsv_img[:, :, 0] = np.clip(hsv_img[:, :, 0] * (hue / 50.0), 0, 180)
-            img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
-
-        # Adjust highlights and shadows
-        if highlights_shadows != 50:
-            img = cv2.convertScaleAbs(img, alpha=1.0, beta=(highlights_shadows - 50) * 5.1)
-
-        # Adjust sharpness
-        if sharpness != 50:
-            kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]]) * (sharpness / 50.0)
-            img = cv2.filter2D(img, -1, kernel)
-
-        # Reduce noise
-        if noise_reduction != 50:
-            img = cv2.fastNlMeansDenoisingColored(img, None, noise_reduction / 50.0 * 10, noise_reduction / 50.0 * 10, 7, 21)
-
-        return img
-
-    def process_image(self, img, apply_bw, brightness, contrast, saturation, white_balance, hue, highlights_shadows, sharpness, noise_reduction, apply_adjustments, generate_final):
-        if not generate_final:
-            preview_img = self.adjust_image(img, apply_bw, brightness, contrast, saturation, white_balance, hue, highlights_shadows, sharpness, noise_reduction)
-            return preview_img
-        
-        adjusted_img = self.adjust_image(img, apply_bw, brightness, contrast, saturation, white_balance, hue, highlights_shadows, sharpness, noise_reduction)
-        result = self.inference(adjusted_img)
-        return (result * 255).astype(np.uint8)
-
-    def load_u2net_model(self):
-        model_path = hf_hub_download(repo_id="Arrcttacsrks/U2net", filename="u2net_portrait.pth", use_auth_token=os.getenv("HF_TOKEN"))
-        net = U2NET(3, 1)
-        net.load_state_dict(torch.load(model_path, map_location="cuda" if torch.cuda.is_available() else "cpu"))
-        net.eval()
-        return net
-
-def main():
-    portrait_generator = PortraitGenerator()
-
-    iface = gr.Interface(
-        fn=portrait_generator.process_image,
-        inputs=[
-            gr.Image(type="numpy", label="Upload your image"),
-            gr.Checkbox(label="Black & White Image"),
-            gr.Slider(0, 100, value=50, label="Brightness"),
-            gr.Slider(0, 100, value=50, label="Contrast"),
-            gr.Slider(0, 100, value=50, label="Saturation"),
-            gr.Slider(0, 100, value=50, label="White Balance"),
-            gr.Slider(0, 100, value=50, label="Hue"),
-            gr.Slider(0, 100, value=50, label="Highlights and Shadows"),
-            gr.Slider(0, 100, value=50, label="Sharpness"),
-            gr.Slider(0, 100, value=50, label="Noise Reduction"),
-            gr.Checkbox(label="Apply Adjustments"),
-            gr.Checkbox(label="Generate Final Portrait")
-        ],
-        outputs=gr.Image(type="numpy", label="Preview or Portrait Result"),
-        title="Portrait Generation with U2NET",
-        description="Upload an image to generate its portrait with optional adjustments. Enable 'Generate Final Portrait' for final output."
-    )
-
-    iface.launch()
-
-if __name__ == "__main__":
-    main()
+# Chuẩn hóa dự đoán
+def normPRED(d):
+    return (d - torch.min(d)) / (torch.max(d) - torch.min(d))
+
+# Hàm suy luận với U2NET
+def inference(net, input_img):
+    input_img = input_img / np.max(input_img)
+    tmpImg = np.zeros((input_img.shape[0], input_img.shape[1], 3))
+    tmpImg[:, :, 0] = (input_img[:, :, 2] - 0.406) / 0.225
+    tmpImg[:, :, 1] = (input_img[:, :, 1] - 0.456) / 0.224
+    tmpImg[:, :, 2] = (input_img[:, :, 0] - 0.485) / 0.229
+    tmpImg = torch.from_numpy(tmpImg.transpose((2, 0, 1))[np.newaxis, :, :, :]).type(torch.FloatTensor)
+    tmpImg = Variable(tmpImg.cuda() if torch.cuda.is_available() else tmpImg)
+    d1, _, _, _, _, _, _ = net(tmpImg)
+    pred = normPRED(1.0 - d1[:, 0, :, :])
+    return pred.cpu().data.numpy().squeeze()
+
+# Hàm chính để xử lý ảnh đầu vào và trả về ảnh chân dung
+def process_image(img, bw_option):
+    # Chuyển đổi ảnh thành đen trắng nếu được chọn
+    if bw_option:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)  # Chuyển lại thành ảnh 3 kênh cho mô hình
+    # Chạy suy luận để tạo ảnh chân dung
+    result = inference(u2net, img)
+    return (result * 255).astype(np.uint8)
+
+# Tải mô hình từ Hugging Face Hub
+def load_u2net_model():
+    model_path = hf_hub_download(repo_id="Arrcttacsrks/U2net", filename="u2net_portrait.pth", use_auth_token=os.getenv("HF_TOKEN"))
+    net = U2NET(3, 1)
+    net.load_state_dict(torch.load(model_path, map_location="cuda" if torch.cuda.is_available() else "cpu"))
+    net.eval()
+    return net
+
+# Khởi tạo mô hình U2NET
+u2net = load_u2net_model()
+
+# Tạo giao diện với Gradio
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="numpy", label="Upload your image"),
+        gr.Checkbox(label="Convert to Black & White?", value=False)  # Thêm tùy chọn tick
+    ],
+    outputs=gr.Image(type="numpy", label="Portrait Result"),
+    title="Portrait Generation with U2NET",
+    description="Upload an image to generate its portrait."
+)
+
+iface.launch()