Upload app(25).py

app(25).py

@@ -0,0 +1,117 @@
+import os
+import cv2
+import torch
+from model import U2NET
+from torch.autograd import Variable
+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()