Spaces:
Running
Running
| from rstor.synthetic_data.interactive.interactive_dead_leaves import generate_deadleave | |
| from rstor.analyzis.interactive.crop import crop_selector, crop, rescale_thumbnail | |
| from rstor.analyzis.interactive.inference import infer | |
| from rstor.analyzis.interactive.degradation import degrade_noise, degrade_blur, downsample, degrade_blur_gaussian, get_blur_kernel | |
| from rstor.analyzis.interactive.model_selection import model_selector | |
| from rstor.analyzis.interactive.images import image_selector | |
| from rstor.analyzis.interactive.metrics import get_metrics, configure_metrics | |
| from interactive_pipe import interactive, KeyboardControl | |
| from typing import Tuple, List | |
| from functools import partial | |
| import numpy as np | |
| get_metrics_restored = partial(get_metrics, image_name="restored") | |
| get_metrics_degraded = partial(get_metrics, image_name="degraded") | |
| def deadleave_inference_pipeline(models_dict: dict) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: | |
| groundtruth = generate_deadleave() | |
| groundtruth = downsample(groundtruth) | |
| model = model_selector(models_dict) | |
| degraded = degrade_blur_gaussian(groundtruth) | |
| degraded = degrade_noise(degraded) | |
| restored = infer(degraded, model) | |
| crop_selector(restored) | |
| groundtruth, degraded, restored = crop(groundtruth, degraded, restored) | |
| configure_metrics() | |
| get_metrics_restored(restored, groundtruth) | |
| get_metrics_degraded(degraded, groundtruth) | |
| return groundtruth, degraded, restored | |
| CANVAS_DICT = { | |
| "demo": [["degraded", "restored"]], | |
| "landscape_light": [["degraded", "restored", "groundtruth"]], | |
| "landscape": [["degraded", "restored", "blur_kernel", "groundtruth"]], | |
| "full": [["degraded", "restored"], ["blur_kernel", "groundtruth"]] | |
| } | |
| CANVAS = list(CANVAS_DICT.keys()) | |
| def morph_canvas(canvas=CANVAS[0], global_params={}): | |
| global_params["__pipeline"].outputs = CANVAS_DICT[canvas] | |
| return None | |
| def visualize_kernel(kernel, global_params={}): | |
| kernel_amplif = kernel.copy() | |
| # kernel_amplif = kernel_amplif - kernel_amplif.min() / (kernel_amplif.max() - kernel_amplif.min()) | |
| kernel_amplif = (kernel_amplif * 10).clip(0, 1) | |
| return kernel_amplif | |
| def natural_inference_pipeline(input_image_list: List[np.ndarray], models_dict: dict): | |
| model = model_selector(models_dict) | |
| img_clean = image_selector(input_image_list) | |
| crop_selector(img_clean) | |
| groundtruth = crop(img_clean) | |
| blur_kernel = get_blur_kernel() | |
| degraded = degrade_blur(groundtruth, blur_kernel) | |
| # degraded = degrade_noise(degraded) | |
| kernel_amplif = visualize_kernel(blur_kernel) | |
| kernel_amplif = rescale_thumbnail(kernel_amplif) | |
| restored = infer(degraded, model) | |
| # configure_metrics() | |
| # get_metrics_restored(restored, groundtruth) | |
| # get_metrics_degraded(degraded, groundtruth) | |
| # morph_canvas() | |
| # return [[degraded, restored], [blur_kernel, groundtruth]] | |
| return [degraded, restored, groundtruth, kernel_amplif] | |