Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,272 Bytes
c295391 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
import numpy as np
from torch.utils.data import Dataset
import cv2
import glob
import os
def get_roi(mask, margin=8):
"""
"""
h0, w0 = mask.shape[:2]
if mask is not None:
rows, cols = np.nonzero(mask)
rowmin, rowmax = np.min(rows), np.max(rows)
colmin, colmax = np.min(cols), np.max(cols)
row, col = rowmax - rowmin, colmax - colmin
flag = not (rowmin - margin <= 0 or rowmax + margin > h0 or
colmin - margin <= 0 or colmax + margin > w0)
if row > col and flag:
r_s, r_e = rowmin - margin, rowmax + margin
c_s, c_e = max(colmin - int(0.5 * (row - col)) - margin, 0), \
min(colmax + int(0.5 * (row - col)) + margin, w0)
elif col >= row and flag:
r_s, r_e = max(rowmin - int(0.5 * (col - row)) - margin, 0), \
min(rowmax + int(0.5 * (col - row)) + margin, h0)
c_s, c_e = colmin - margin, colmax + margin
else:
r_s, r_e, c_s, c_e = 0, h0, 0, w0
else:
r_s, r_e, c_s, c_e = 0, h0, 0, w0
return np.array([h0, w0, r_s, r_e, c_s, c_e])
def crop_and_resize_img(img, roi, max_image_resolution=6000):
h0, w0, r_s, r_e, c_s, c_e = roi
img = img[r_s:r_e, c_s:c_e, :]
h = max(512, min(max_image_resolution, (max(img.shape[:2]) // 512) * 512))
w = h
img = cv2.resize(img, (w, h), interpolation=cv2.INTER_CUBIC)
bit_depth = 255.0 if img.dtype == np.uint8 else 65535.0 if img.dtype == np.uint16 else 1.0
img = np.float32(img) / bit_depth
return img
def crop_and_resize_mask(mask, roi, max_image_resolution=6000):
h0, w0, r_s, r_e, c_s, c_e = roi
mask = mask[r_s:r_e, c_s:c_e]
h = max(512, min(max_image_resolution, (max(mask.shape[:2]) // 512) * 512))
w = h
mask = np.float32(cv2.resize(mask, (w, h), interpolation=cv2.INTER_CUBIC) > 0.5)
return mask
class DemoData(Dataset):
def __init__(self,input_imgs_list,input_mask):
self.input_imgs_list = input_imgs_list
self.input_mask = input_mask
def __len__(self):
return 1
def load(self,input_images_list,mask):
if mask is None:
mask = np.ones_like(input_images_list[0][0])
else:
mask = np.array(mask)
mask = mask[:,:,0]
if mask.max() <= 1.0:
self.mask_original = mask[:,:,None]
else:
self.mask_original = mask[:,:,None] / 255.0
self.roi = get_roi(mask)
for i in range(len(input_images_list)):
img = input_images_list[i]
input_images_list[i]= crop_and_resize_img(img[0], self.roi)
I = np.array(input_images_list)
numberofimages,h,w,_ = I.shape
mask = crop_and_resize_mask(mask, self.roi)
I = np.reshape(I, (-1, h * w, 3))
temp = np.mean(I[:, mask.flatten()==1,:], axis=2)
mx = np.max(temp, axis=1)
temp = mx
I /= (temp.reshape(-1,1,1) + 1.0e-6)
I = np.transpose(I, (1, 2, 0))
I = I.reshape(h, w, 3, numberofimages)
mask = (mask.reshape(h, w, 1)).astype(np.float32)
h = mask.shape[0]
w = mask.shape[1]
self.h = h
self.w = w
self.I = I
self.N = np.ones((h, w, 3), np.float32)
self.mask = mask
return 1
def __getitem__(self, idx):
self.load(self.input_imgs_list,self.input_mask)
return {
"imgs":self.I.transpose(2,0,1,3),
"mask":self.mask.transpose(2,0,1),
"mask_original":self.mask_original.transpose(2,0,1),
"roi":self.roi
}
class TestData(Dataset):
def __init__(
self,
data_root: list = None,
numofimages: int = 16
):
self.data_root = data_root
self.numberOfImages = numofimages
self.objlist = []
for i in range(len(self.data_root)):
with os.scandir(self.data_root[i]) as entries:
self.objlist += [entry.path for entry in entries if entry.is_dir()]
print(f"[Dataset] => {len(self.objlist)} items selected.")
objlist = self.objlist
total = len(objlist)
indices = list(range(total))
self.objlist = [objlist[i] for i in indices]
print(f"Test, => {len(self.objlist)} items selected.")
def load(self, objlist, dirid):
obj_path = objlist[dirid]
if "DiLiGenT" in obj_path:
nml_path = os.path.join(obj_path, "Normal_gt.png")
if "10" not in obj_path: # diligent
directlist = sorted(glob.glob(os.path.join(obj_path, f"0*")))
else: # diligent100
directlist = sorted([
path for path in glob.glob(os.path.join(obj_path, "*.png"))
if not os.path.basename(path).lower() == "mask.png"
])
elif "LUCES" in obj_path:
nml_path = os.path.join(obj_path, "normals.png")
directlist = sorted([
f for i in range(1, 52) for f in glob.glob(os.path.join(obj_path, f"{i:02d}*"))
])
elif "Real" in obj_path:
nml_path = os.path.join(obj_path, "Normal_gt.png")
directlist = sorted(glob.glob(os.path.join(obj_path, f"L*")))
else:
print(f"error:unknown dataset{obj_path}")
return 0
num_images_to_sample = self.numberOfImages
if num_images_to_sample is not None and num_images_to_sample < len(directlist):
indexset = np.random.permutation(len(directlist))[:num_images_to_sample]
else:
indexset = range(len(directlist))
I = None
mask = None
N = None
n_true = None
for i, indexofimage in enumerate(indexset):
img_path = directlist[indexofimage]
read_img = cv2.imread(img_path, flags=cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
if read_img is None:
print(f"warning: can not read {img_path}")
return 0
img = cv2.cvtColor(read_img, cv2.COLOR_BGR2RGB)
if i == 0:
mask_path = os.path.join(obj_path, "mask.png")
if os.path.exists(mask_path):
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) / 255.0
else:
mask = np.ones_like(read_img)[:,:,0]
if os.path.exists(nml_path):
bit_depth = 65535.0 if "LUCES" in obj_path else 255.0
N = cv2.cvtColor(cv2.imread(nml_path, flags=cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH), cv2.COLOR_BGR2RGB) / bit_depth
N = 2 * N - 1
N = N / np.linalg.norm(N, axis=2, keepdims=True)
N = N * mask[:, :, np.newaxis]
n_true = N
self.roi = get_roi(mask)
mask = crop_and_resize_mask(mask, self.roi)
img= crop_and_resize_img(img, self.roi)
h, w = img.shape[:2]
if i == 0:
I = np.zeros((len(indexset), h, w, 3), np.float32)
I[i, :, :, :] = img
imgs_ = I.copy()
I = np.reshape(I, (-1, h * w, 3))
"""Data Normalization"""
temp = np.mean(I[:, mask.flatten()==1,:], axis=2)
mean = np.mean(temp, axis=1)
mx = np.max(temp, axis=1)
scale = np.random.rand(I.shape[0],)
temp = (1-scale) * mean + scale * mx
imgs_ /= (temp.reshape(-1,1,1,1) + 1.0e-6)
I = imgs_
I = np.transpose(I, (1, 2, 3, 0))
mask = (mask.reshape(h, w, 1)).astype(np.float32)
h = mask.shape[0]
w = mask.shape[1]
self.h = h
self.w = w
self.I = I #
if ("DiLiGenT" in obj_path and "10" in obj_path) or "Real" in obj_path: # diligent100
self.N = np.ones((h,w,3,1))
else:
self.N = n_true[:,:,:,np.newaxis]
self.mask = mask
self.directlist = directlist
return 1
def __getitem__(self, index_):
objid = index_
while 1:
success = self.load(self.objlist, objid)
if success:
break
else:
objid = np.random.randint(0, len(self.objlist))
img = self.I.transpose(2,0,1,3) # 3 h w Nmax
nml = self.N.transpose(2,0,1,3) # 3 h w 1
objname = os.path.basename(os.path.basename(self.objlist[objid]))
numberOfImages = self.numberOfImages
try:
output = {
'imgs': img,
'nml': nml,
"mask":self.mask.transpose(2,0,1),
'directlist': self.directlist,
'objname': objname,
'numberOfImages': numberOfImages,
"roi":self.roi
}
return output
except:
raise KeyError
def __len__(self):
return len(self.objlist) |