import random import numpy as np import cv2 import torch import torch.utils.data as data import data.util as util from PIL import Image, ImageOps from io import BytesIO import torchvision.transforms.functional as F import lmdb import pyarrow # Reads full-quality images and pulls tiles from them. Also extracts LR renderings of the full image with cues as to # where those tiles came from. class LmdbDatasetWithRef(data.Dataset): def __init__(self, opt): super(LmdbDatasetWithRef, self).__init__() self.opt = opt self.db = lmdb.open(self.opt['lmdb_path'], subdir=True, readonly=True, lock=False, readahead=False, meminit=False) self.data_type = 'img' self.force_multiple = self.opt['force_multiple'] if 'force_multiple' in self.opt.keys() else 1 with self.db.begin(write=False) as txn: self.keys = pyarrow.deserialize(txn.get(b'__keys__')) self.len = pyarrow.deserialize(txn.get(b'__len__'))\ def motion_blur(self, image, size, angle): k = np.zeros((size, size), dtype=np.float32) k[(size - 1) // 2, :] = np.ones(size, dtype=np.float32) k = cv2.warpAffine(k, cv2.getRotationMatrix2D((size / 2 - 0.5, size / 2 - 0.5), angle, 1.0), (size, size)) k = k * (1.0 / np.sum(k)) return cv2.filter2D(image, -1, k) def resize_point(self, point, orig_dim, new_dim): oh, ow = orig_dim nh, nw = new_dim dh, dw = float(nh) / float(oh), float(nw) / float(ow) point[0] = int(dh * float(point[0])) point[1] = int(dw * float(point[1])) return point def augment_tile(self, img_GT, img_LQ, strength=1): scale = self.opt['scale'] GT_size = self.opt['target_size'] H, W, _ = img_GT.shape assert H >= GT_size and W >= GT_size LQ_size = GT_size // scale img_LQ = cv2.resize(img_LQ, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR) img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR) if self.opt['use_blurring']: # Pick randomly between gaussian, motion, or no blur. blur_det = random.randint(0, 100) blur_magnitude = 3 if 'blur_magnitude' not in self.opt.keys() else self.opt['blur_magnitude'] blur_magnitude = max(1, int(blur_magnitude*strength)) if blur_det < 40: blur_sig = int(random.randrange(0, int(blur_magnitude))) img_LQ = cv2.GaussianBlur(img_LQ, (blur_magnitude, blur_magnitude), blur_sig) elif blur_det < 70: img_LQ = self.motion_blur(img_LQ, random.randrange(1, int(blur_magnitude) * 3), random.randint(0, 360)) return img_GT, img_LQ # Converts img_LQ to PIL and performs JPG compression corruptions and grayscale on the image, then returns it. def pil_augment(self, img_LQ, strength=1): img_LQ = (img_LQ * 255).astype(np.uint8) img_LQ = Image.fromarray(img_LQ) if self.opt['use_compression_artifacts'] and random.random() > .25: sub_lo = 90 * strength sub_hi = 30 * strength qf = random.randrange(100 - sub_lo, 100 - sub_hi) corruption_buffer = BytesIO() img_LQ.save(corruption_buffer, "JPEG", quality=qf, optimice=True) corruption_buffer.seek(0) img_LQ = Image.open(corruption_buffer) if 'grayscale' in self.opt.keys() and self.opt['grayscale']: img_LQ = ImageOps.grayscale(img_LQ).convert('RGB') return img_LQ def __getitem__(self, index): scale = self.opt['scale'] # get the hq image and the ref image key = self.keys[index] ref_key = key[:key.index('_')] with self.db.begin(write=False) as txn: bytes_ref = txn.get(ref_key.encode()) bytes_tile = txn.get(key.encode()) unpacked_ref = pyarrow.deserialize(bytes_ref) unpacked_tile = pyarrow.deserialize(bytes_tile) gt_fullsize_ref = unpacked_ref[0] img_GT, gt_center = unpacked_tile # TODO: synthesize gt_mask. gt_mask = np.ones(img_GT.shape[:2]) orig_gt_dim = gt_fullsize_ref.shape[:2] # Synthesize LQ by downsampling. if self.opt['phase'] == 'train': GT_size = self.opt['target_size'] random_scale = random.choice(self.random_scale_list) if len(img_GT.shape) == 2: print("ERRAR:") print(img_GT.shape) print(full_path) H_s, W_s, _ = img_GT.shape def _mod(n, random_scale, scale, thres): rlt = int(n * random_scale) rlt = (rlt // scale) * scale return thres if rlt < thres else rlt H_s = _mod(H_s, random_scale, scale, GT_size) W_s = _mod(W_s, random_scale, scale, GT_size) img_GT = cv2.resize(img_GT, (W_s, H_s), interpolation=cv2.INTER_LINEAR) if img_GT.ndim == 2: img_GT = cv2.cvtColor(img_GT, cv2.COLOR_GRAY2BGR) H, W, _ = img_GT.shape # using matlab imresize img_LQ = util.imresize_np(img_GT, 1 / scale, True) lq_fullsize_ref = util.imresize_np(gt_fullsize_ref, 1 / scale, True) if img_LQ.ndim == 2: img_LQ = np.expand_dims(img_LQ, axis=2) lq_mask, lq_center = gt_mask, self.resize_point(gt_center.clone(), orig_gt_dim, lq_fullsize_ref.shape[:2]) orig_lq_dim = lq_fullsize_ref.shape[:2] # Enforce force_resize constraints via clipping. h, w, _ = img_LQ.shape if h % self.force_multiple != 0 or w % self.force_multiple != 0: h, w = (h - h % self.force_multiple), (w - w % self.force_multiple) img_LQ = img_LQ[:h, :w, :] lq_fullsize_ref = lq_fullsize_ref[:h, :w, :] h *= scale w *= scale img_GT = img_GT[:h, :w] gt_fullsize_ref = gt_fullsize_ref[:h, :w, :] if self.opt['phase'] == 'train': img_GT, img_LQ = self.augment_tile(img_GT, img_LQ) gt_fullsize_ref, lq_fullsize_ref = self.augment_tile(gt_fullsize_ref, lq_fullsize_ref, strength=.2) # Scale masks. lq_mask = cv2.resize(lq_mask, (lq_fullsize_ref.shape[1], lq_fullsize_ref.shape[0]), interpolation=cv2.INTER_LINEAR) gt_mask = cv2.resize(gt_mask, (gt_fullsize_ref.shape[1], gt_fullsize_ref.shape[0]), interpolation=cv2.INTER_LINEAR) # Scale center coords lq_center = self.resize_point(lq_center, orig_lq_dim, lq_fullsize_ref.shape[:2]) gt_center = self.resize_point(gt_center, orig_gt_dim, gt_fullsize_ref.shape[:2]) # BGR to RGB, HWC to CHW, numpy to tensor if img_GT.shape[2] == 3: img_GT = cv2.cvtColor(img_GT, cv2.COLOR_BGR2RGB) img_LQ = cv2.cvtColor(img_LQ, cv2.COLOR_BGR2RGB) lq_fullsize_ref = cv2.cvtColor(lq_fullsize_ref, cv2.COLOR_BGR2RGB) gt_fullsize_ref = cv2.cvtColor(gt_fullsize_ref, cv2.COLOR_BGR2RGB) # LQ needs to go to a PIL image to perform the compression-artifact transformation. if self.opt['phase'] == 'train': img_LQ = self.pil_augment(img_LQ) lq_fullsize_ref = self.pil_augment(lq_fullsize_ref, strength=.2) img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float() gt_fullsize_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(gt_fullsize_ref, (2, 0, 1)))).float() img_LQ = F.to_tensor(img_LQ) lq_fullsize_ref = F.to_tensor(lq_fullsize_ref) lq_mask = torch.from_numpy(np.ascontiguousarray(lq_mask)).unsqueeze(dim=0) gt_mask = torch.from_numpy(np.ascontiguousarray(gt_mask)).unsqueeze(dim=0) if 'lq_noise' in self.opt.keys(): lq_noise = torch.randn_like(img_LQ) * self.opt['lq_noise'] / 255 img_LQ += lq_noise lq_fullsize_ref += lq_noise # Apply the masks to the full images. gt_fullsize_ref = torch.cat([gt_fullsize_ref, gt_mask], dim=0) lq_fullsize_ref = torch.cat([lq_fullsize_ref, lq_mask], dim=0) d = {'LQ': img_LQ, 'GT': img_GT, 'gt_fullsize_ref': gt_fullsize_ref, 'lq_fullsize_ref': lq_fullsize_ref, 'lq_center': lq_center, 'gt_center': gt_center, 'LQ_path': key, 'GT_path': key} return d def __len__(self): return self.len if __name__ == '__main__': opt = { 'name': 'amalgam', 'lmdb_path': 'F:\\4k6k\\datasets\\ns_images\\imagesets\\imagesets-lmdb-ref', 'use_flip': True, 'use_compression_artifacts': True, 'use_blurring': True, 'use_rot': True, 'lq_noise': 5, 'target_size': 128, 'min_tile_size': 256, 'scale': 2, 'phase': 'train' } ''' opt = { 'name': 'amalgam', 'dataroot_GT': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imagesets-lmdb-ref'], 'dataroot_GT_weights': [1], 'force_multiple': 32, 'scale': 2, 'phase': 'test' } ''' ds = LmdbDatasetWithRef(opt) import os os.makedirs("debug", exist_ok=True) for i in range(300, len(ds)): print(i) o = ds[i] for k, v in o.items(): if 'path' not in k: #if 'full' in k: #masked = v[:3, :, :] * v[3] #torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k)) #v = v[:3, :, :] import torchvision torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))