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| import numpy as np | |
| import cv2 | |
| import onnxruntime | |
| import gradio as gr | |
| def pre_process(img: np.array) -> np.array: | |
| # H, W, C -> C, H, W | |
| img = np.transpose(img[:, :, 0:3], (2, 0, 1)) | |
| # C, H, W -> 1, C, H, W | |
| img = np.expand_dims(img, axis=0).astype(np.float32) | |
| return img | |
| def post_process(img: np.array) -> np.array: | |
| # 1, C, H, W -> C, H, W | |
| img = np.squeeze(img) | |
| # C, H, W -> H, W, C | |
| img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8) | |
| return img | |
| def inference(model_path: str, img_array: np.array) -> np.array: | |
| ort_session = onnxruntime.InferenceSession(model_path) | |
| ort_inputs = {ort_session.get_inputs()[0].name: img_array} | |
| ort_outs = ort_session.run(None, ort_inputs) | |
| return ort_outs[0] | |
| def convert_pil_to_cv2(image): | |
| # pil_image = image.convert("RGB") | |
| open_cv_image = np.array(image) | |
| # RGB to BGR | |
| open_cv_image = open_cv_image[:, :, ::-1].copy() | |
| return open_cv_image | |
| def upscale(image): | |
| model_path = "models/model.ort" | |
| img = convert_pil_to_cv2(image) | |
| if img.ndim == 2: | |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) | |
| if img.shape[2] == 4: | |
| alpha = img[:, :, 3] # GRAY | |
| alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR) # BGR | |
| alpha_output = post_process(inference(model_path, pre_process(alpha))) # BGR | |
| alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY) # GRAY | |
| img = img[:, :, 0:3] # BGR | |
| image_output = post_process(inference(model_path, pre_process(img))) # BGR | |
| image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA) # BGRA | |
| image_output[:, :, 3] = alpha_output | |
| elif img.shape[2] == 3: | |
| image_output = post_process(inference(model_path, pre_process(img))) # BGR | |
| return image_output | |
| examples = [f"examples/example_{i+1}.png" for i in range(5)] | |
| css = ".output-image, .input-image, .image-preview {height: 480px !important} " | |
| gr.Interface( | |
| fn=upscale, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs="image", | |
| examples=examples, | |
| title="Image Upscaling 🦆", | |
| allow_flagging="never", | |
| css=css, | |
| ).launch() | |