import cv2 import math import numpy as np import os import torch from basicsr.archs.rrdbnet_arch import RRDBNet from torch.hub import download_url_to_file, get_dir from torch.nn import functional as F from urllib.parse import urlparse ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) class RealESRGANer(): def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False): self.scale = scale self.tile_size = tile self.tile_pad = tile_pad self.pre_pad = pre_pad self.mod_scale = None self.half = half # initialize model self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale) if model_path.startswith('https://'): model_path = load_file_from_url( url=model_path, model_dir='realesrgan/weights', progress=True, file_name=None) loadnet = torch.load(model_path) if 'params_ema' in loadnet: keyname = 'params_ema' else: keyname = 'params' model.load_state_dict(loadnet[keyname], strict=True) model.eval() self.model = model.to(self.device) if self.half: self.model = self.model.half() def pre_process(self, img): img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float() self.img = img.unsqueeze(0).to(self.device) if self.half: self.img = self.img.half() # pre_pad if self.pre_pad != 0: self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect') # mod pad if self.scale == 2: self.mod_scale = 2 elif self.scale == 1: self.mod_scale = 4 if self.mod_scale is not None: self.mod_pad_h, self.mod_pad_w = 0, 0 _, _, h, w = self.img.size() if (h % self.mod_scale != 0): self.mod_pad_h = (self.mod_scale - h % self.mod_scale) if (w % self.mod_scale != 0): self.mod_pad_w = (self.mod_scale - w % self.mod_scale) self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect') def process(self): self.output = self.model(self.img) def tile_process(self): """Modified from: https://github.com/ata4/esrgan-launcher """ batch, channel, height, width = self.img.shape output_height = height * self.scale output_width = width * self.scale output_shape = (batch, channel, output_height, output_width) # start with black image self.output = self.img.new_zeros(output_shape) tiles_x = math.ceil(width / self.tile_size) tiles_y = math.ceil(height / self.tile_size) # loop over all tiles for y in range(tiles_y): for x in range(tiles_x): # extract tile from input image ofs_x = x * self.tile_size ofs_y = y * self.tile_size # input tile area on total image input_start_x = ofs_x input_end_x = min(ofs_x + self.tile_size, width) input_start_y = ofs_y input_end_y = min(ofs_y + self.tile_size, height) # input tile area on total image with padding input_start_x_pad = max(input_start_x - self.tile_pad, 0) input_end_x_pad = min(input_end_x + self.tile_pad, width) input_start_y_pad = max(input_start_y - self.tile_pad, 0) input_end_y_pad = min(input_end_y + self.tile_pad, height) # input tile dimensions input_tile_width = input_end_x - input_start_x input_tile_height = input_end_y - input_start_y tile_idx = y * tiles_x + x + 1 input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad] # upscale tile try: with torch.no_grad(): output_tile = self.model(input_tile) except Exception as error: print('Error', error) print(f'\tTile {tile_idx}/{tiles_x * tiles_y}') # output tile area on total image output_start_x = input_start_x * self.scale output_end_x = input_end_x * self.scale output_start_y = input_start_y * self.scale output_end_y = input_end_y * self.scale # output tile area without padding output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale output_end_x_tile = output_start_x_tile + input_tile_width * self.scale output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale output_end_y_tile = output_start_y_tile + input_tile_height * self.scale # put tile into output image self.output[:, :, output_start_y:output_end_y, output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile, output_start_x_tile:output_end_x_tile] def post_process(self): # remove extra pad if self.mod_scale is not None: _, _, h, w = self.output.size() self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale] # remove prepad if self.pre_pad != 0: _, _, h, w = self.output.size() self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale] return self.output @torch.no_grad() def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'): h_input, w_input = img.shape[0:2] # img: numpy img = img.astype(np.float32) if np.max(img) > 255: # 16-bit image max_range = 65535 print('\tInput is a 16-bit image') else: max_range = 255 img = img / max_range if len(img.shape) == 2: # gray image img_mode = 'L' img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) elif img.shape[2] == 4: # RGBA image with alpha channel img_mode = 'RGBA' alpha = img[:, :, 3] img = img[:, :, 0:3] img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) if alpha_upsampler == 'realesrgan': alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB) else: img_mode = 'RGB' img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # ------------------- process image (without the alpha channel) ------------------- # self.pre_process(img) if self.tile_size > 0: self.tile_process() else: self.process() output_img = self.post_process() output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy() output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0)) if img_mode == 'L': output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY) # ------------------- process the alpha channel if necessary ------------------- # if img_mode == 'RGBA': if alpha_upsampler == 'realesrgan': self.pre_process(alpha) if self.tile_size > 0: self.tile_process() else: self.process() output_alpha = self.post_process() output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy() output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0)) output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY) else: h, w = alpha.shape[0:2] output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR) # merge the alpha channel output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA) output_img[:, :, 3] = output_alpha # ------------------------------ return ------------------------------ # if max_range == 65535: # 16-bit image output = (output_img * 65535.0).round().astype(np.uint16) else: output = (output_img * 255.0).round().astype(np.uint8) if outscale is not None and outscale != float(self.scale): output = cv2.resize( output, ( int(w_input * outscale), int(h_input * outscale), ), interpolation=cv2.INTER_LANCZOS4) return output, img_mode def load_file_from_url(url, model_dir=None, progress=True, file_name=None): """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py """ if model_dir is None: hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file