| import gradio as gr |
| import cv2 |
| import numpy as np |
| from PIL import Image |
| import os |
| import zipfile |
| import tempfile |
| import shutil |
|
|
| def process_image(img, strength, upscale=False): |
| if upscale: |
| new_size = (img.width * 2, img.height * 2) |
| img = img.resize(new_size, resample=Image.LANCZOS) |
|
|
| img_cv = np.array(img.convert('L')) |
| img_cv = img_cv.astype(np.float32) / 255.0 |
|
|
| sobelx = cv2.Sobel(img_cv, cv2.CV_64F, 1, 0, ksize=3) |
| sobely = cv2.Sobel(img_cv, cv2.CV_64F, 0, 1, ksize=3) |
|
|
|
|
| nx = -sobelx * strength |
| ny = -sobely * strength |
| nz = np.ones_like(img_cv) |
|
|
|
|
| length = np.sqrt(nx**2 + ny**2 + nz**2) |
| nx /= length |
| ny /= length |
| nz /= length |
|
|
|
|
| nx = (nx + 1) / 2 * 255 |
| ny = (ny + 1) / 2 * 255 |
| nz = (nz + 1) / 2 * 255 |
|
|
| normal_map = np.stack((nx, ny, nz), axis=-1).astype(np.uint8) |
| return Image.fromarray(normal_map) |
|
|
| def process_single(image, strength, upscale): |
| if image is None: |
| return None |
| return process_image(image, strength, upscale) |
|
|
| def process_files(file_objs, strength, upscale): |
| if not file_objs: |
| return None |
| |
| |
| temp_dir = tempfile.mkdtemp() |
| zip_filename = "normal_maps.zip" |
| zip_path = os.path.join(tempfile.gettempdir(), zip_filename) |
| |
| with zipfile.ZipFile(zip_path, 'w') as zipf: |
| for file_obj in file_objs: |
| file_path = file_obj.name if hasattr(file_obj, 'name') else file_obj |
| file_name = os.path.basename(file_path) |
| |
| if file_name.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.webp')): |
| try: |
| img = Image.open(file_path) |
| normal_map = process_image(img, strength, upscale) |
| |
| base_name, ext = os.path.splitext(file_name) |
| out_name = f"{base_name}_normalmap{ext}" |
| |
| out_path = os.path.join(temp_dir, out_name) |
| try: |
| normal_map.save(out_path) |
| except: |
| out_name = f"{base_name}_normalmap.png" |
| out_path = os.path.join(temp_dir, out_name) |
| normal_map.save(out_path, format="PNG") |
| |
| zipf.write(out_path, arcname=out_name) |
| except Exception as e: |
| print(f"Error ({file_path}): {e}") |
| |
| |
| shutil.rmtree(temp_dir) |
| return zip_path |
|
|
| |
| with gr.Blocks(title="Normal Map Generator", theme=gr.themes.Soft()) as demo: |
| gr.Markdown("# 🗺️ Normal Map Generator") |
| gr.Markdown("Create normal maps from standard images for 3D modeling and texturing.") |
| |
| with gr.Tabs(): |
| |
| with gr.Tab("Single Image"): |
| with gr.Row(): |
| with gr.Column(): |
| img_input = gr.Image(type="pil", label="Input Image") |
| strength_slider_1 = gr.Slider(minimum=0.1, maximum=10.0, value=2.0, step=0.1, label="Normal Map Strength") |
| upscale_check_1 = gr.Checkbox(label="Upscale 2x (High Quality Smoothing)", value=False) |
| btn_single = gr.Button("Generate", variant="primary") |
| with gr.Column(): |
| img_output = gr.Image(type="pil", label="Normal Map Result", format="png") |
| |
| btn_single.click(fn=process_single, inputs=[img_input, strength_slider_1, upscale_check_1], outputs=img_output) |
| |
| |
| with gr.Tab("Folder (Batch Processing)"): |
| gr.Markdown("Select a folder or drag and drop multiple images. All images will be processed and provided as a ZIP file.") |
| with gr.Row(): |
| with gr.Column(): |
| |
| folder_input = gr.File(file_count="directory", label="Upload Folder / Files") |
| strength_slider_2 = gr.Slider(minimum=0.1, maximum=10.0, value=2.0, step=0.1, label="Normal Map Strength") |
| upscale_check_2 = gr.Checkbox(label="Upscale 2x (High Quality Smoothing)", value=False) |
| btn_folder = gr.Button("Generate Batch and Download", variant="primary") |
| with gr.Column(): |
| file_output = gr.File(label="Download ZIP File") |
| |
| btn_folder.click(fn=process_files, inputs=[folder_input, strength_slider_2, upscale_check_2], outputs=file_output) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
