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Reformat $EVERYTHING

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CptCaptain
2021-11-15 16:53:30 +01:00
parent 56f2aa7d5d
commit 43df77fb9b
32 changed files with 4171 additions and 3749 deletions
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data/commons.py
+69 -67
View File
@@ -4,107 +4,109 @@ import cv2
def get_patterns(path='syn', imsizes=[], crop=True):
pattern_size = imsizes[0]
if path == 'syn':
np.random.seed(42)
pattern = np.random.uniform(0,1, size=pattern_size)
pattern = (pattern < 0.1).astype(np.float32)
pattern.reshape(*imsizes[0])
else:
pattern = cv2.imread(path)
pattern = pattern.astype(np.float32)
pattern /= 255
if pattern.ndim == 2:
pattern = np.stack([pattern for idx in range(3)], axis=2)
if crop and pattern.shape[0] > pattern_size[0] and pattern.shape[1] > pattern_size[1]:
r0 = (pattern.shape[0] - pattern_size[0]) // 2
c0 = (pattern.shape[1] - pattern_size[1]) // 2
pattern = pattern[r0:r0+imsizes[0][0], c0:c0+imsizes[0][1]]
patterns = []
for imsize in imsizes:
pat = cv2.resize(pattern, (imsize[1],imsize[0]), interpolation=cv2.INTER_LINEAR)
patterns.append(pat)
pattern_size = imsizes[0]
if path == 'syn':
np.random.seed(42)
pattern = np.random.uniform(0, 1, size=pattern_size)
pattern = (pattern < 0.1).astype(np.float32)
pattern.reshape(*imsizes[0])
else:
pattern = cv2.imread(path)
pattern = pattern.astype(np.float32)
pattern /= 255
if pattern.ndim == 2:
pattern = np.stack([pattern for idx in range(3)], axis=2)
if crop and pattern.shape[0] > pattern_size[0] and pattern.shape[1] > pattern_size[1]:
r0 = (pattern.shape[0] - pattern_size[0]) // 2
c0 = (pattern.shape[1] - pattern_size[1]) // 2
pattern = pattern[r0:r0 + imsizes[0][0], c0:c0 + imsizes[0][1]]
patterns = []
for imsize in imsizes:
pat = cv2.resize(pattern, (imsize[1], imsize[0]), interpolation=cv2.INTER_LINEAR)
patterns.append(pat)
return patterns
return patterns
def get_rotation_matrix(v0, v1):
v0 = v0/np.linalg.norm(v0)
v1 = v1/np.linalg.norm(v1)
v = np.cross(v0,v1)
c = np.dot(v0,v1)
s = np.linalg.norm(v)
I = np.eye(3)
vXStr = '{} {} {}; {} {} {}; {} {} {}'.format(0, -v[2], v[1], v[2], 0, -v[0], -v[1], v[0], 0)
k = np.matrix(vXStr)
r = I + k + k @ k * ((1 -c)/(s**2))
return np.asarray(r.astype(np.float32))
v0 = v0 / np.linalg.norm(v0)
v1 = v1 / np.linalg.norm(v1)
v = np.cross(v0, v1)
c = np.dot(v0, v1)
s = np.linalg.norm(v)
I = np.eye(3)
vXStr = '{} {} {}; {} {} {}; {} {} {}'.format(0, -v[2], v[1], v[2], 0, -v[0], -v[1], v[0], 0)
k = np.matrix(vXStr)
r = I + k + k @ k * ((1 - c) / (s ** 2))
return np.asarray(r.astype(np.float32))
def augment_image(img,rng,disp=None,grad=None,max_shift=64,max_blur=1.5,max_noise=10.0,max_sp_noise=0.001):
def augment_image(img, rng, disp=None, grad=None, max_shift=64, max_blur=1.5, max_noise=10.0, max_sp_noise=0.001):
# get min/max values of image
min_val = np.min(img)
max_val = np.max(img)
# init augmented image
img_aug = img
# init disparity correction map
disp_aug = disp
grad_aug = grad
# apply affine transformation
if max_shift>1:
if max_shift > 1:
# affine parameters
rows,cols = img.shape
rows, cols = img.shape
shear = 0
shift = 0
shear_correction = 0
if rng.uniform(0,1)<0.75: shear = rng.uniform(-max_shift,max_shift) # shear with 75% probability
else: shift = rng.uniform(0,max_shift) # shift with 25% probability
if shear<0: shear_correction = -shear
if rng.uniform(0, 1) < 0.75:
shear = rng.uniform(-max_shift, max_shift) # shear with 75% probability
else:
shift = rng.uniform(0, max_shift) # shift with 25% probability
if shear < 0: shear_correction = -shear
# affine transformation
a = shear/float(rows)
b = shift+shear_correction
a = shear / float(rows)
b = shift + shear_correction
# warp image
T = np.float32([[1,a,b],[0,1,0]])
img_aug = cv2.warpAffine(img_aug,T,(cols,rows))
T = np.float32([[1, a, b], [0, 1, 0]])
img_aug = cv2.warpAffine(img_aug, T, (cols, rows))
if grad is not None:
grad_aug = cv2.warpAffine(grad,T,(cols,rows))
grad_aug = cv2.warpAffine(grad, T, (cols, rows))
# disparity correction map
col = a*np.array(range(rows))+b
disp_delta = np.tile(col,(cols,1)).transpose()
col = a * np.array(range(rows)) + b
disp_delta = np.tile(col, (cols, 1)).transpose()
if disp is not None:
disp_aug = cv2.warpAffine(disp+disp_delta,T,(cols,rows))
disp_aug = cv2.warpAffine(disp + disp_delta, T, (cols, rows))
# gaussian smoothing
if rng.uniform(0,1)<0.5:
img_aug = cv2.GaussianBlur(img_aug,(5,5),rng.uniform(0.2,max_blur))
if rng.uniform(0, 1) < 0.5:
img_aug = cv2.GaussianBlur(img_aug, (5, 5), rng.uniform(0.2, max_blur))
# per-pixel gaussian noise
img_aug = img_aug + rng.randn(*img_aug.shape)*rng.uniform(0.0,max_noise)/255.0
img_aug = img_aug + rng.randn(*img_aug.shape) * rng.uniform(0.0, max_noise) / 255.0
# salt-and-pepper noise
if rng.uniform(0,1)<0.5:
ratio=rng.uniform(0.0,max_sp_noise)
if rng.uniform(0, 1) < 0.5:
ratio = rng.uniform(0.0, max_sp_noise)
img_shape = img_aug.shape
img_aug = img_aug.flatten()
coord = rng.choice(np.size(img_aug), int(np.size(img_aug)*ratio))
coord = rng.choice(np.size(img_aug), int(np.size(img_aug) * ratio))
img_aug[coord] = max_val
coord = rng.choice(np.size(img_aug), int(np.size(img_aug)*ratio))
coord = rng.choice(np.size(img_aug), int(np.size(img_aug) * ratio))
img_aug[coord] = min_val
img_aug = np.reshape(img_aug, img_shape)
# clip intensities back to [0,1]
img_aug = np.maximum(img_aug,0.0)
img_aug = np.minimum(img_aug,1.0)
img_aug = np.maximum(img_aug, 0.0)
img_aug = np.minimum(img_aug, 1.0)
# return image
return img_aug, disp_aug, grad_aug
data/create_syn_data.py
+220 -222
View File
@@ -10,261 +10,259 @@ import cv2
import os
import collections
import sys
sys.path.append('../')
import renderer
import co
from commons import get_patterns,get_rotation_matrix
from commons import get_patterns, get_rotation_matrix
from lcn import lcn
def get_objs(shapenet_dir, obj_classes, num_perclass=100):
shapenet = {'chair': '03001627',
'airplane': '02691156',
'car': '02958343',
'watercraft': '04530566'}
shapenet = {'chair': '03001627',
'airplane': '02691156',
'car': '02958343',
'watercraft': '04530566'}
obj_paths = []
for cls in obj_classes:
if cls not in shapenet.keys():
raise Exception('unknown class name')
ids = shapenet[cls]
obj_path = sorted(Path(f'{shapenet_dir}/{ids}').glob('**/models/*.obj'))
obj_paths += obj_path[:num_perclass]
print(f'found {len(obj_paths)} object paths')
obj_paths = []
for cls in obj_classes:
if cls not in shapenet.keys():
raise Exception('unknown class name')
ids = shapenet[cls]
obj_path = sorted(Path(f'{shapenet_dir}/{ids}').glob('**/models/*.obj'))
obj_paths += obj_path[:num_perclass]
print(f'found {len(obj_paths)} object paths')
objs = []
for obj_path in obj_paths:
print(f'load {obj_path}')
v, f, _, n = co.io3d.read_obj(obj_path)
diffs = v.max(axis=0) - v.min(axis=0)
v /= (0.5 * diffs.max())
v -= (v.min(axis=0) + 1)
f = f.astype(np.int32)
objs.append((v, f, n))
print(f'loaded {len(objs)} objects')
objs = []
for obj_path in obj_paths:
print(f'load {obj_path}')
v, f, _, n = co.io3d.read_obj(obj_path)
diffs = v.max(axis=0) - v.min(axis=0)
v /= (0.5 * diffs.max())
v -= (v.min(axis=0) + 1)
f = f.astype(np.int32)
objs.append((v,f,n))
print(f'loaded {len(objs)} objects')
return objs
return objs
def get_mesh(rng, min_z=0):
# set up background board
verts, faces, normals, colors = [], [], [], []
v, f, n = co.geometry.xyplane(z=0, interleaved=True)
v[:,2] += -v[:,2].min() + rng.uniform(2,7)
v[:,:2] *= 5e2
v[:,2] = np.mean(v[:,2]) + (v[:,2] - np.mean(v[:,2])) * 5e2
c = np.empty_like(v)
c[:] = rng.uniform(0,1, size=(3,)).astype(np.float32)
verts.append(v)
faces.append(f)
normals.append(n)
colors.append(c)
# randomly sample 4 foreground objects for each scene
for shape_idx in range(4):
v, f, n = objs[rng.randint(0,len(objs))]
v, f, n = v.copy(), f.copy(), n.copy()
s = rng.uniform(0.25, 1)
v *= s
R = co.geometry.rotm_from_quat(co.geometry.quat_random(rng=rng))
v = v @ R.T
n = n @ R.T
v[:,2] += -v[:,2].min() + min_z + rng.uniform(0.5, 3)
v[:,:2] += rng.uniform(-1, 1, size=(1,2))
# set up background board
verts, faces, normals, colors = [], [], [], []
v, f, n = co.geometry.xyplane(z=0, interleaved=True)
v[:, 2] += -v[:, 2].min() + rng.uniform(2, 7)
v[:, :2] *= 5e2
v[:, 2] = np.mean(v[:, 2]) + (v[:, 2] - np.mean(v[:, 2])) * 5e2
c = np.empty_like(v)
c[:] = rng.uniform(0,1, size=(3,)).astype(np.float32)
verts.append(v.astype(np.float32))
c[:] = rng.uniform(0, 1, size=(3,)).astype(np.float32)
verts.append(v)
faces.append(f)
normals.append(n)
colors.append(c)
verts, faces = co.geometry.stack_mesh(verts, faces)
normals = np.vstack(normals).astype(np.float32)
colors = np.vstack(colors).astype(np.float32)
return verts, faces, colors, normals
# randomly sample 4 foreground objects for each scene
for shape_idx in range(4):
v, f, n = objs[rng.randint(0, len(objs))]
v, f, n = v.copy(), f.copy(), n.copy()
s = rng.uniform(0.25, 1)
v *= s
R = co.geometry.rotm_from_quat(co.geometry.quat_random(rng=rng))
v = v @ R.T
n = n @ R.T
v[:, 2] += -v[:, 2].min() + min_z + rng.uniform(0.5, 3)
v[:, :2] += rng.uniform(-1, 1, size=(1, 2))
c = np.empty_like(v)
c[:] = rng.uniform(0, 1, size=(3,)).astype(np.float32)
verts.append(v.astype(np.float32))
faces.append(f)
normals.append(n)
colors.append(c)
verts, faces = co.geometry.stack_mesh(verts, faces)
normals = np.vstack(normals).astype(np.float32)
colors = np.vstack(colors).astype(np.float32)
return verts, faces, colors, normals
def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length=4):
tic = time.time()
rng = np.random.RandomState()
tic = time.time()
rng = np.random.RandomState()
rng.seed(idx)
rng.seed(idx)
verts, faces, colors, normals = get_mesh(rng)
data = renderer.PyRenderInput(verts=verts.copy(), colors=colors.copy(), normals=normals.copy(), faces=faces.copy())
print(f'loading mesh for sample {idx + 1}/{n_samples} took {time.time() - tic}[s]')
verts, faces, colors, normals = get_mesh(rng)
data = renderer.PyRenderInput(verts=verts.copy(), colors=colors.copy(), normals=normals.copy(), faces=faces.copy())
print(f'loading mesh for sample {idx+1}/{n_samples} took {time.time()-tic}[s]')
# let the camera point to the center
center = np.array([0, 0, 3], dtype=np.float32)
basevec = np.array([-baseline, 0, 0], dtype=np.float32)
unit = np.array([0, 0, 1], dtype=np.float32)
cam_x_ = rng.uniform(-0.2, 0.2)
cam_y_ = rng.uniform(-0.2, 0.2)
cam_z_ = rng.uniform(-0.2, 0.2)
ret = collections.defaultdict(list)
blend_im_rnd = np.clip(blend_im + rng.uniform(-0.1, 0.1), 0, 1)
# capture the same static scene from different view points as a track
for ind in range(track_length):
cam_x = cam_x_ + rng.uniform(-0.1, 0.1)
cam_y = cam_y_ + rng.uniform(-0.1, 0.1)
cam_z = cam_z_ + rng.uniform(-0.1, 0.1)
tcam = np.array([cam_x, cam_y, cam_z], dtype=np.float32)
if np.linalg.norm(tcam[0:2]) < 1e-9:
Rcam = np.eye(3, dtype=np.float32)
else:
Rcam = get_rotation_matrix(center, center - tcam)
tproj = tcam + basevec
Rproj = Rcam
ret['R'].append(Rcam)
ret['t'].append(tcam)
cams = []
projs = []
# render the scene at multiple scales
scales = [1, 0.5, 0.25, 0.125]
for scale in scales:
fx = K[0, 0] * scale
fy = K[1, 1] * scale
px = K[0, 2] * scale
py = K[1, 2] * scale
im_height = imsize[0] * scale
im_width = imsize[1] * scale
cams.append(renderer.PyCamera(fx, fy, px, py, Rcam, tcam, im_width, im_height))
projs.append(renderer.PyCamera(fx, fy, px, py, Rproj, tproj, im_width, im_height))
for s, cam, proj, pattern in zip(itertools.count(), cams, projs, patterns):
fl = K[0, 0] / (2 ** s)
shader = renderer.PyShader(0.5, 1.5, 0.0, 10)
pyrenderer = renderer.PyRenderer(cam, shader, engine='gpu')
pyrenderer.mesh_proj(data, proj, pattern, d_alpha=0, d_beta=0.35)
# get the reflected laser pattern $R$
im = pyrenderer.color().copy()
depth = pyrenderer.depth().copy()
disp = baseline * fl / depth
mask = depth > 0
im = np.mean(im, axis=2)
# get the ambient image $A$
ambient = pyrenderer.normal().copy()
ambient = np.mean(ambient, axis=2)
# get the noise free IR image $J$
im = blend_im_rnd * im + (1 - blend_im_rnd) * ambient
ret[f'ambient{s}'].append(ambient[None].astype(np.float32))
# get the gradient magnitude of the ambient image $|\nabla A|$
ambient = ambient.astype(np.float32)
sobelx = cv2.Sobel(ambient, cv2.CV_32F, 1, 0, ksize=5)
sobely = cv2.Sobel(ambient, cv2.CV_32F, 0, 1, ksize=5)
grad = np.sqrt(sobelx ** 2 + sobely ** 2)
grad = np.maximum(grad - 0.8, 0.0) # parameter
# get the local contract normalized grad LCN($|\nabla A|$)
grad_lcn, grad_std = lcn.normalize(grad, 5, 0.1)
grad_lcn = np.clip(grad_lcn, 0.0, 1.0) # parameter
ret[f'grad{s}'].append(grad_lcn[None].astype(np.float32))
ret[f'im{s}'].append(im[None].astype(np.float32))
ret[f'mask{s}'].append(mask[None].astype(np.float32))
ret[f'disp{s}'].append(disp[None].astype(np.float32))
for key in ret.keys():
ret[key] = np.stack(ret[key], axis=0)
# save to files
out_dir = out_root / f'{idx:08d}'
out_dir.mkdir(exist_ok=True, parents=True)
for k, val in ret.items():
for tidx in range(track_length):
v = val[tidx]
out_path = out_dir / f'{k}_{tidx}.npy'
np.save(out_path, v)
np.save(str(out_dir / 'blend_im.npy'), blend_im_rnd)
print(f'create sample {idx + 1}/{n_samples} took {time.time() - tic}[s]')
# let the camera point to the center
center = np.array([0,0,3], dtype=np.float32)
if __name__ == '__main__':
basevec = np.array([-baseline,0,0], dtype=np.float32)
unit = np.array([0,0,1],dtype=np.float32)
np.random.seed(42)
cam_x_ = rng.uniform(-0.2,0.2)
cam_y_ = rng.uniform(-0.2,0.2)
cam_z_ = rng.uniform(-0.2,0.2)
# output directory
with open('../config.json') as fp:
config = json.load(fp)
data_root = Path(config['DATA_ROOT'])
shapenet_root = config['SHAPENET_ROOT']
ret = collections.defaultdict(list)
blend_im_rnd = np.clip(blend_im + rng.uniform(-0.1,0.1), 0,1)
data_type = 'syn'
out_root = data_root / f'{data_type}'
out_root.mkdir(parents=True, exist_ok=True)
# capture the same static scene from different view points as a track
for ind in range(track_length):
cam_x = cam_x_ + rng.uniform(-0.1,0.1)
cam_y = cam_y_ + rng.uniform(-0.1,0.1)
cam_z = cam_z_ + rng.uniform(-0.1,0.1)
tcam = np.array([cam_x, cam_y, cam_z], dtype=np.float32)
if np.linalg.norm(tcam[0:2])<1e-9:
Rcam = np.eye(3, dtype=np.float32)
start = 0
if len(sys.argv) >= 2 and isinstance(sys.argv[2], int):
start = sys.argv[2]
else:
Rcam = get_rotation_matrix(center, center-tcam)
if sys.argv[2] == '--resume':
try:
start = max([int(dir) for dir in os.listdir(out_root) if str.isdigit(dir)]) or 0
except:
pass
tproj = tcam + basevec
Rproj = Rcam
# load shapenet models
obj_classes = ['chair']
objs = get_objs(shapenet_root, obj_classes)
ret['R'].append(Rcam)
ret['t'].append(tcam)
# camera parameters
imsize = (488, 648)
imsizes = [(imsize[0] // (2 ** s), imsize[1] // (2 ** s)) for s in range(4)]
# K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32)
K = np.array([[1929.5936336276382, 0, 113.66561071478046], [0, 1911.2517985448746, 473.70108079885887], [0, 0, 1]],
dtype=np.float32)
focal_lengths = [K[0, 0] / (2 ** s) for s in range(4)]
baseline = 0.075
blend_im = 0.6
noise = 0
cams = []
projs = []
# capture the same static scene from different view points as a track
track_length = 4
# render the scene at multiple scales
scales = [1, 0.5, 0.25, 0.125]
# load pattern image
pattern_path = './kinect_pattern.png'
pattern_crop = True
patterns = get_patterns(pattern_path, imsizes, pattern_crop)
for scale in scales:
fx = K[0,0] * scale
fy = K[1,1] * scale
px = K[0,2] * scale
py = K[1,2] * scale
im_height = imsize[0] * scale
im_width = imsize[1] * scale
cams.append( renderer.PyCamera(fx,fy,px,py, Rcam, tcam, im_width, im_height) )
projs.append( renderer.PyCamera(fx,fy,px,py, Rproj, tproj, im_width, im_height) )
# write settings to file
settings = {
'imsizes': imsizes,
'patterns': patterns,
'focal_lengths': focal_lengths,
'baseline': baseline,
'K': K,
}
out_path = out_root / f'settings.pkl'
print(f'write settings to {out_path}')
with open(str(out_path), 'wb') as f:
pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL)
for s, cam, proj, pattern in zip(itertools.count(), cams, projs, patterns):
fl = K[0,0] / (2**s)
shader = renderer.PyShader(0.5,1.5,0.0,10)
pyrenderer = renderer.PyRenderer(cam, shader, engine='gpu')
pyrenderer.mesh_proj(data, proj, pattern, d_alpha=0, d_beta=0.35)
# get the reflected laser pattern $R$
im = pyrenderer.color().copy()
depth = pyrenderer.depth().copy()
disp = baseline * fl / depth
mask = depth > 0
im = np.mean(im, axis=2)
# get the ambient image $A$
ambient = pyrenderer.normal().copy()
ambient = np.mean(ambient, axis=2)
# get the noise free IR image $J$
im = blend_im_rnd * im + (1 - blend_im_rnd) * ambient
ret[f'ambient{s}'].append( ambient[None].astype(np.float32) )
# get the gradient magnitude of the ambient image $|\nabla A|$
ambient = ambient.astype(np.float32)
sobelx = cv2.Sobel(ambient,cv2.CV_32F,1,0,ksize=5)
sobely = cv2.Sobel(ambient,cv2.CV_32F,0,1,ksize=5)
grad = np.sqrt(sobelx**2 + sobely**2)
grad = np.maximum(grad-0.8,0.0) # parameter
# get the local contract normalized grad LCN($|\nabla A|$)
grad_lcn, grad_std = lcn.normalize(grad,5,0.1)
grad_lcn = np.clip(grad_lcn,0.0,1.0) # parameter
ret[f'grad{s}'].append( grad_lcn[None].astype(np.float32))
ret[f'im{s}'].append( im[None].astype(np.float32))
ret[f'mask{s}'].append(mask[None].astype(np.float32))
ret[f'disp{s}'].append(disp[None].astype(np.float32))
for key in ret.keys():
ret[key] = np.stack(ret[key], axis=0)
# save to files
out_dir = out_root / f'{idx:08d}'
out_dir.mkdir(exist_ok=True, parents=True)
for k,val in ret.items():
for tidx in range(track_length):
v = val[tidx]
out_path = out_dir / f'{k}_{tidx}.npy'
np.save(out_path, v)
np.save( str(out_dir /'blend_im.npy'), blend_im_rnd)
print(f'create sample {idx+1}/{n_samples} took {time.time()-tic}[s]')
if __name__=='__main__':
np.random.seed(42)
# output directory
with open('../config.json') as fp:
config = json.load(fp)
data_root = Path(config['DATA_ROOT'])
shapenet_root = config['SHAPENET_ROOT']
data_type = 'syn'
out_root = data_root / f'{data_type}'
out_root.mkdir(parents=True, exist_ok=True)
start = 0
if len(sys.argv) >= 2 and isinstance(sys.argv[2], int):
start = sys.argv[2]
else:
if sys.argv[2] == '--resume':
try:
start = max([int(dir) for dir in os.listdir(out_root) if str.isdigit(dir)]) or 0
except:
pass
# load shapenet models
obj_classes = ['chair']
objs = get_objs(shapenet_root, obj_classes)
# camera parameters
imsize = (488, 648)
imsizes = [(imsize[0]//(2**s), imsize[1]//(2**s)) for s in range(4)]
# K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32)
K = np.array([[1929.5936336276382, 0, 113.66561071478046], [0, 1911.2517985448746, 473.70108079885887], [0 ,0, 1]], dtype=np.float32)
focal_lengths = [K[0,0]/(2**s) for s in range(4)]
baseline=0.075
blend_im = 0.6
noise = 0
# capture the same static scene from different view points as a track
track_length = 4
# load pattern image
pattern_path = './kinect_pattern.png'
pattern_crop = True
patterns = get_patterns(pattern_path, imsizes, pattern_crop)
# write settings to file
settings = {
'imsizes': imsizes,
'patterns': patterns,
'focal_lengths': focal_lengths,
'baseline': baseline,
'K': K,
}
out_path = out_root / f'settings.pkl'
print(f'write settings to {out_path}')
with open(str(out_path), 'wb') as f:
pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL)
# start the job
n_samples = 2**10 + 2**13
for idx in range(start, n_samples):
args = (out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length)
create_data(*args)
# start the job
n_samples = 2 ** 10 + 2 ** 13
for idx in range(start, n_samples):
args = (out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length)
create_data(*args)
data/dataset.py
+109 -109
View File
@@ -21,128 +21,128 @@ from .commons import get_patterns, augment_image
from mpl_toolkits.mplot3d import Axes3D
class TrackSynDataset(torchext.BaseDataset):
'''
Load locally saved synthetic dataset
Please run ./create_syn_data.sh to generate the dataset
'''
def __init__(self, settings_path, sample_paths, track_length=2, train=True, data_aug=False):
super().__init__(train=train)
'''
Load locally saved synthetic dataset
Please run ./create_syn_data.sh to generate the dataset
'''
self.settings_path = settings_path
self.sample_paths = sample_paths
self.data_aug = data_aug
self.train = train
self.track_length=track_length
assert(track_length<=4)
def __init__(self, settings_path, sample_paths, track_length=2, train=True, data_aug=False):
super().__init__(train=train)
with open(str(settings_path), 'rb') as f:
settings = pickle.load(f)
self.imsizes = settings['imsizes']
self.patterns = settings['patterns']
self.focal_lengths = settings['focal_lengths']
self.baseline = settings['baseline']
self.K = settings['K']
self.settings_path = settings_path
self.sample_paths = sample_paths
self.data_aug = data_aug
self.train = train
self.track_length = track_length
assert (track_length <= 4)
self.scale = len(self.imsizes)
with open(str(settings_path), 'rb') as f:
settings = pickle.load(f)
self.imsizes = settings['imsizes']
self.patterns = settings['patterns']
self.focal_lengths = settings['focal_lengths']
self.baseline = settings['baseline']
self.K = settings['K']
self.max_shift=0
self.max_blur=0.5
self.max_noise=3.0
self.max_sp_noise=0.0005
self.scale = len(self.imsizes)
def __len__(self):
return len(self.sample_paths)
self.max_shift = 0
self.max_blur = 0.5
self.max_noise = 3.0
self.max_sp_noise = 0.0005
def __getitem__(self, idx):
if not self.train:
rng = self.get_rng(idx)
else:
rng = np.random.RandomState()
sample_path = self.sample_paths[idx]
def __len__(self):
return len(self.sample_paths)
if self.train:
track_ind = np.random.permutation(4)[0:self.track_length]
else:
track_ind = [0]
ret = {}
ret['id'] = idx
# load imgs, at all scales
for sidx in range(len(self.imsizes)):
imgs = []
ambs = []
grads = []
for tidx in track_ind:
imgs.append(np.load(os.path.join(sample_path,f'im{sidx}_{tidx}.npy')))
ambs.append(np.load(os.path.join(sample_path,f'ambient{sidx}_{tidx}.npy')))
grads.append(np.load(os.path.join(sample_path,f'grad{sidx}_{tidx}.npy')))
ret[f'im{sidx}'] = np.stack(imgs, axis=0)
ret[f'ambient{sidx}'] = np.stack(ambs, axis=0)
ret[f'grad{sidx}'] = np.stack(grads, axis=0)
# load disp and grad only at full resolution
disps = []
R = []
t = []
for tidx in track_ind:
disps.append(np.load(os.path.join(sample_path,f'disp0_{tidx}.npy')))
R.append(np.load(os.path.join(sample_path,f'R_{tidx}.npy')))
t.append(np.load(os.path.join(sample_path,f't_{tidx}.npy')))
ret[f'disp0'] = np.stack(disps, axis=0)
ret['R'] = np.stack(R, axis=0)
ret['t'] = np.stack(t, axis=0)
blend_im = np.load(os.path.join(sample_path,'blend_im.npy'))
ret['blend_im'] = blend_im.astype(np.float32)
#### apply data augmentation at different scales seperately, only work for max_shift=0
if self.data_aug:
for sidx in range(len(self.imsizes)):
if sidx==0:
img = ret[f'im{sidx}']
disp = ret[f'disp{sidx}']
grad = ret[f'grad{sidx}']
img_aug = np.zeros_like(img)
disp_aug = np.zeros_like(img)
grad_aug = np.zeros_like(img)
for i in range(img.shape[0]):
img_aug_, disp_aug_, grad_aug_ = augment_image(img[i,0],rng,
disp=disp[i,0],grad=grad[i,0],
max_shift=self.max_shift, max_blur=self.max_blur,
max_noise=self.max_noise, max_sp_noise=self.max_sp_noise)
img_aug[i] = img_aug_[None].astype(np.float32)
disp_aug[i] = disp_aug_[None].astype(np.float32)
grad_aug[i] = grad_aug_[None].astype(np.float32)
ret[f'im{sidx}'] = img_aug
ret[f'disp{sidx}'] = disp_aug
ret[f'grad{sidx}'] = grad_aug
def __getitem__(self, idx):
if not self.train:
rng = self.get_rng(idx)
else:
img = ret[f'im{sidx}']
img_aug = np.zeros_like(img)
for i in range(img.shape[0]):
img_aug_, _, _ = augment_image(img[i,0],rng,
max_shift=self.max_shift, max_blur=self.max_blur,
max_noise=self.max_noise, max_sp_noise=self.max_sp_noise)
img_aug[i] = img_aug_[None].astype(np.float32)
ret[f'im{sidx}'] = img_aug
rng = np.random.RandomState()
sample_path = self.sample_paths[idx]
if len(track_ind)==1:
for key, val in ret.items():
if key!='blend_im' and key!='id':
ret[key] = val[0]
if self.train:
track_ind = np.random.permutation(4)[0:self.track_length]
else:
track_ind = [0]
ret = {}
ret['id'] = idx
return ret
# load imgs, at all scales
for sidx in range(len(self.imsizes)):
imgs = []
ambs = []
grads = []
for tidx in track_ind:
imgs.append(np.load(os.path.join(sample_path, f'im{sidx}_{tidx}.npy')))
ambs.append(np.load(os.path.join(sample_path, f'ambient{sidx}_{tidx}.npy')))
grads.append(np.load(os.path.join(sample_path, f'grad{sidx}_{tidx}.npy')))
ret[f'im{sidx}'] = np.stack(imgs, axis=0)
ret[f'ambient{sidx}'] = np.stack(ambs, axis=0)
ret[f'grad{sidx}'] = np.stack(grads, axis=0)
def getK(self, sidx=0):
K = self.K.copy() / (2**sidx)
K[2,2] = 1
return K
# load disp and grad only at full resolution
disps = []
R = []
t = []
for tidx in track_ind:
disps.append(np.load(os.path.join(sample_path, f'disp0_{tidx}.npy')))
R.append(np.load(os.path.join(sample_path, f'R_{tidx}.npy')))
t.append(np.load(os.path.join(sample_path, f't_{tidx}.npy')))
ret[f'disp0'] = np.stack(disps, axis=0)
ret['R'] = np.stack(R, axis=0)
ret['t'] = np.stack(t, axis=0)
blend_im = np.load(os.path.join(sample_path, 'blend_im.npy'))
ret['blend_im'] = blend_im.astype(np.float32)
#### apply data augmentation at different scales seperately, only work for max_shift=0
if self.data_aug:
for sidx in range(len(self.imsizes)):
if sidx == 0:
img = ret[f'im{sidx}']
disp = ret[f'disp{sidx}']
grad = ret[f'grad{sidx}']
img_aug = np.zeros_like(img)
disp_aug = np.zeros_like(img)
grad_aug = np.zeros_like(img)
for i in range(img.shape[0]):
img_aug_, disp_aug_, grad_aug_ = augment_image(img[i, 0], rng,
disp=disp[i, 0], grad=grad[i, 0],
max_shift=self.max_shift, max_blur=self.max_blur,
max_noise=self.max_noise,
max_sp_noise=self.max_sp_noise)
img_aug[i] = img_aug_[None].astype(np.float32)
disp_aug[i] = disp_aug_[None].astype(np.float32)
grad_aug[i] = grad_aug_[None].astype(np.float32)
ret[f'im{sidx}'] = img_aug
ret[f'disp{sidx}'] = disp_aug
ret[f'grad{sidx}'] = grad_aug
else:
img = ret[f'im{sidx}']
img_aug = np.zeros_like(img)
for i in range(img.shape[0]):
img_aug_, _, _ = augment_image(img[i, 0], rng,
max_shift=self.max_shift, max_blur=self.max_blur,
max_noise=self.max_noise, max_sp_noise=self.max_sp_noise)
img_aug[i] = img_aug_[None].astype(np.float32)
ret[f'im{sidx}'] = img_aug
if len(track_ind) == 1:
for key, val in ret.items():
if key != 'blend_im' and key != 'id':
ret[key] = val[0]
return ret
def getK(self, sidx=0):
K = self.K.copy() / (2 ** sidx)
K[2, 2] = 1
return K
if __name__ == '__main__':
pass
pass
data/lcn/lcn.html
+292 -103
View File
@@ -2,7 +2,7 @@
<!-- Generated by Cython 0.29 -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Cython: lcn.pyx</title>
<style type="text/css">
@@ -355,17 +355,23 @@ body.cython { font-family: courier; font-size: 12; }
.cython .vi { color: #19177C } /* Name.Variable.Instance */
.cython .vm { color: #19177C } /* Name.Variable.Magic */
.cython .il { color: #666666 } /* Literal.Number.Integer.Long */
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</head>
<body class="cython">
<p><span style="border-bottom: solid 1px grey;">Generated by Cython 0.29</span></p>
<p>
<span style="background-color: #FFFF00">Yellow lines</span> hint at Python interaction.<br />
<span style="background-color: #FFFF00">Yellow lines</span> hint at Python interaction.<br/>
Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it.
</p>
<p>Raw output: <a href="lcn.c">lcn.c</a></p>
<div class="cython"><pre class="cython line score-16" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">01</span>: <span class="k">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span></pre>
<pre class='cython code score-16 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>
<div class="cython">
<pre class="cython line score-16"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">01</span>: <span class="k">import</span> <span class="nn">numpy</span> <span
class="k">as</span> <span class="nn">np</span></pre>
<pre class='cython code score-16 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span
class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
@@ -374,22 +380,39 @@ body.cython { font-family: courier; font-size: 12; }
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
</pre><pre class="cython line score-0">&#xA0;<span class="">02</span>: <span class="k">cimport</span> <span class="nn">cython</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">03</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">04</span>: <span class="c"># use c square root function</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">05</span>: <span class="k">cdef</span> <span class="kr">extern</span> <span class="k">from</span> <span class="s">&quot;math.h&quot;</span><span class="p">:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">06</span>: <span class="nb">float</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">float</span> <span class="n">x</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">07</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">08</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">boundscheck</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">09</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">wraparound</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">10</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">cdivision</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">11</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">12</span>: <span class="c"># 3 parameters:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">13</span>: <span class="c"># - float image</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">14</span>: <span class="c"># - kernel size (actually this is the radius, kernel is 2*k+1)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">15</span>: <span class="c"># - small constant epsilon that is used to avoid division by zero</span></pre>
<pre class="cython line score-67" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">16</span>: <span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="nb">float</span><span class="p">[:,</span> <span class="p">:]</span> <span class="n">img</span><span class="p">,</span> <span class="nb">int</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mf">4</span><span class="p">,</span> <span class="nb">float</span> <span class="n">epsilon</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">):</span></pre>
<pre class='cython code score-67 '>/* Python wrapper */
</pre>
<pre class="cython line score-0">&#xA0;<span class="">02</span>: <span class="k">cimport</span> <span class="nn">cython</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">03</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">04</span>: <span class="c"># use c square root function</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">05</span>: <span class="k">cdef</span> <span
class="kr">extern</span> <span class="k">from</span> <span class="s">&quot;math.h&quot;</span><span
class="p">:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">06</span>: <span class="nb">float</span> <span class="n">sqrt</span><span
class="p">(</span><span class="nb">float</span> <span class="n">x</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">07</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">08</span>: <span class="nd">@cython</span><span
class="o">.</span><span class="n">boundscheck</span><span class="p">(</span><span
class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">09</span>: <span class="nd">@cython</span><span
class="o">.</span><span class="n">wraparound</span><span class="p">(</span><span
class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">10</span>: <span class="nd">@cython</span><span
class="o">.</span><span class="n">cdivision</span><span class="p">(</span><span class="bp">True</span><span
class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">11</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">12</span>: <span class="c"># 3 parameters:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">13</span>: <span class="c"># - float image</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">14</span>: <span class="c"># - kernel size (actually this is the radius, kernel is 2*k+1)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">15</span>: <span class="c"># - small constant epsilon that is used to avoid division by zero</span></pre>
<pre class="cython line score-67"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">16</span>: <span class="k">def</span> <span class="nf">normalize</span><span
class="p">(</span><span class="nb">float</span><span class="p">[:,</span> <span class="p">:]</span> <span
class="n">img</span><span class="p">,</span> <span class="nb">int</span> <span class="n">kernel_size</span> <span
class="o">=</span> <span class="mf">4</span><span class="p">,</span> <span class="nb">float</span> <span
class="n">epsilon</span> <span class="o">=</span> <span class="mf">0.01</span><span
class="p">):</span></pre>
<pre class='cython code score-67 '>/* Python wrapper */
static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyMethodDef __pyx_mdef_3lcn_1normalize = {"normalize", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_3lcn_1normalize, METH_VARARGS|METH_KEYWORDS, 0};
static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
@@ -434,7 +457,8 @@ static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_
}
}
if (unlikely(kw_args &gt; 0)) {
if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "normalize") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 16, __pyx_L3_error)</span>
if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "normalize") &lt; 0)) <span
class='error_goto'>__PYX_ERR(0, 16, __pyx_L3_error)</span>
}
} else {
switch (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)) {
@@ -447,21 +471,27 @@ static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_
default: goto __pyx_L5_argtuple_error;
}
}
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<pre class="cython line score-0">&#xA0;<span class="">17</span>: </pre>
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onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">19</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
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<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">20</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
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<pre class="cython line score-46"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">24</span>: <span class="n">img_std</span> <span class="o">=</span> <span
class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span
class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span
class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span
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<pre class='cython code score-46 '> <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_t_5, __pyx_n_s_np);<span
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onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">25</span>: <span class="k">cdef</span> <span class="kt">float</span>[<span class="p">:,</span> <span
class="p">:]</span> <span class="n">img_lcn_view</span> <span class="o">=</span> <span
class="n">img_lcn</span></pre>
<pre class='cython code score-2 '> __pyx_t_6 = <span
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class="p">:]</span> <span class="n">img_std_view</span> <span class="o">=</span> <span
class="n">img_std</span></pre>
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class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_float</span>(__pyx_v_img_std, PyBUF_WRITABLE);<span
class='error_goto'> if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 26, __pyx_L1_error)</span>
__pyx_v_img_std_view = __pyx_t_6;
__pyx_t_6.memview = NULL;
__pyx_t_6.data = NULL;
</pre><pre class="cython line score-0">&#xA0;<span class="">27</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">28</span>: <span class="c"># temporary c variables</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">29</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">tmp</span><span class="p">,</span> <span class="nf">mean</span><span class="p">,</span> <span class="nf">stddev</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">30</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">m</span><span class="p">,</span> <span class="nf">n</span><span class="p">,</span> <span class="nf">i</span><span class="p">,</span> <span class="nf">j</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">31</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">ks</span> <span class="o">=</span> <span class="n">kernel_size</span></pre>
<pre class='cython code score-0 '> __pyx_v_ks = __pyx_v_kernel_size;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">32</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">eps</span> <span class="o">=</span> <span class="n">epsilon</span></pre>
<pre class='cython code score-0 '> __pyx_v_eps = __pyx_v_epsilon;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">33</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">num</span> <span class="o">=</span> <span class="p">(</span><span class="n">ks</span><span class="o">*</span><span class="mf">2</span><span class="o">+</span><span class="mf">1</span><span class="p">)</span><span class="o">**</span><span class="mf">2</span></pre>
<pre class='cython code score-0 '> __pyx_v_num = __Pyx_pow_Py_ssize_t(((__pyx_v_ks * 2) + 1), 2);
</pre><pre class="cython line score-0">&#xA0;<span class="">34</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">35</span>: <span class="c"># for all pixels do</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">36</span>: <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span class="n">M</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_7 = (__pyx_v_M - __pyx_v_ks);
</pre>
<pre class="cython line score-0">&#xA0;<span class="">27</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">28</span>: <span class="c"># temporary c variables</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">29</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
class="nf">tmp</span><span class="p">,</span> <span class="nf">mean</span><span class="p">,</span> <span
class="nf">stddev</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">30</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">m</span><span class="p">,</span> <span class="nf">n</span><span class="p">,</span> <span
class="nf">i</span><span class="p">,</span> <span class="nf">j</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">31</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">ks</span> <span class="o">=</span> <span class="n">kernel_size</span></pre>
<pre class='cython code score-0 '> __pyx_v_ks = __pyx_v_kernel_size;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">32</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
class="nf">eps</span> <span class="o">=</span> <span class="n">epsilon</span></pre>
<pre class='cython code score-0 '> __pyx_v_eps = __pyx_v_epsilon;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">33</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
class="nf">num</span> <span class="o">=</span> <span class="p">(</span><span class="n">ks</span><span
class="o">*</span><span class="mf">2</span><span class="o">+</span><span class="mf">1</span><span class="p">)</span><span
class="o">**</span><span class="mf">2</span></pre>
<pre class='cython code score-0 '> __pyx_v_num = __Pyx_pow_Py_ssize_t(((__pyx_v_ks * 2) + 1), 2);
</pre>
<pre class="cython line score-0">&#xA0;<span class="">34</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">35</span>: <span
class="c"># for all pixels do</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">36</span>: <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span
class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span
class="n">M</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_7 = (__pyx_v_M - __pyx_v_ks);
__pyx_t_8 = __pyx_t_7;
for (__pyx_t_9 = __pyx_v_ks; __pyx_t_9 &lt; __pyx_t_8; __pyx_t_9+=1) {
__pyx_v_m = __pyx_t_9;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">37</span>: <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span class="n">N</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_10 = (__pyx_v_N - __pyx_v_ks);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">37</span>: <span class="k">for</span> <span class="n">n</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span
class="p">,</span><span class="n">N</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_10 = (__pyx_v_N - __pyx_v_ks);
__pyx_t_11 = __pyx_t_10;
for (__pyx_t_12 = __pyx_v_ks; __pyx_t_12 &lt; __pyx_t_11; __pyx_t_12+=1) {
__pyx_v_n = __pyx_t_12;
</pre><pre class="cython line score-0">&#xA0;<span class="">38</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">39</span>: <span class="c"># calculate mean</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">40</span>: <span class="n">mean</span> <span class="o">=</span> <span class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = 0.0;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">41</span>: <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
</pre>
<pre class="cython line score-0">&#xA0;<span class="">38</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">39</span>: <span class="c"># calculate mean</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">40</span>: <span class="n">mean</span> <span class="o">=</span> <span
class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = 0.0;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">41</span>: <span class="k">for</span> <span class="n">i</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
__pyx_t_14 = __pyx_t_13;
for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {
__pyx_v_i = __pyx_t_15;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">42</span>: <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">42</span>: <span class="k">for</span> <span class="n">j</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
__pyx_t_17 = __pyx_t_16;
for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) {
__pyx_v_j = __pyx_t_18;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">43</span>: <span class="n">mean</span> <span class="o">+=</span> <span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span></pre>
<pre class='cython code score-0 '> __pyx_t_19 = (__pyx_v_m + __pyx_v_i);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">43</span>: <span class="n">mean</span> <span class="o">+=</span> <span
class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span
class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span
class="p">]</span></pre>
<pre class='cython code score-0 '> __pyx_t_19 = (__pyx_v_m + __pyx_v_i);
__pyx_t_20 = (__pyx_v_n + __pyx_v_j);
__pyx_v_mean = (__pyx_v_mean + (*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_19 * __pyx_v_img.strides[0]) ) + __pyx_t_20 * __pyx_v_img.strides[1]) ))));
}
}
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">44</span>: <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="o">/</span><span class="n">num</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = (__pyx_v_mean / __pyx_v_num);
</pre><pre class="cython line score-0">&#xA0;<span class="">45</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">46</span>: <span class="c"># calculate std dev</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">47</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = 0.0;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">48</span>: <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">44</span>: <span class="n">mean</span> <span class="o">=</span> <span
class="n">mean</span><span class="o">/</span><span class="n">num</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = (__pyx_v_mean / __pyx_v_num);
</pre>
<pre class="cython line score-0">&#xA0;<span class="">45</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">46</span>: <span
class="c"># calculate std dev</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">47</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = 0.0;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">48</span>: <span class="k">for</span> <span class="n">i</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
__pyx_t_14 = __pyx_t_13;
for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {
__pyx_v_i = __pyx_t_15;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">49</span>: <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">49</span>: <span class="k">for</span> <span class="n">j</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
__pyx_t_17 = __pyx_t_16;
for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) {
__pyx_v_j = __pyx_t_18;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">50</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="n">stddev</span> <span class="o">+</span> <span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_21 = (__pyx_v_m + __pyx_v_i);
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">50</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="n">stddev</span> <span class="o">+</span> <span class="p">(</span><span class="n">img</span><span
class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span
class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span
class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span
class="o">*</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span
class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span
class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span
class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_21 = (__pyx_v_m + __pyx_v_i);
__pyx_t_22 = (__pyx_v_n + __pyx_v_j);
__pyx_t_23 = (__pyx_v_m + __pyx_v_i);
__pyx_t_24 = (__pyx_v_n + __pyx_v_j);
__pyx_v_stddev = (__pyx_v_stddev + (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_21 * __pyx_v_img.strides[0]) ) + __pyx_t_22 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) * ((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_23 * __pyx_v_img.strides[0]) ) + __pyx_t_24 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean)));
}
}
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">51</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">stddev</span><span class="o">/</span><span class="n">num</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = sqrt((__pyx_v_stddev / __pyx_v_num));
</pre><pre class="cython line score-0">&#xA0;<span class="">52</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">53</span>: <span class="c"># compute normalized image (add epsilon) and std dev image</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">54</span>: <span class="n">img_lcn_view</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">stddev</span><span class="o">+</span><span class="n">eps</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_25 = __pyx_v_m;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">51</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="n">sqrt</span><span class="p">(</span><span class="n">stddev</span><span class="o">/</span><span
class="n">num</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = sqrt((__pyx_v_stddev / __pyx_v_num));
</pre>
<pre class="cython line score-0">&#xA0;<span class="">52</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">53</span>: <span class="c"># compute normalized image (add epsilon) and std dev image</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">54</span>: <span class="n">img_lcn_view</span><span class="p">[</span><span
class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span
class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span
class="n">n</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span
class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">stddev</span><span
class="o">+</span><span class="n">eps</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_25 = __pyx_v_m;
__pyx_t_26 = __pyx_v_n;
__pyx_t_27 = __pyx_v_m;
__pyx_t_28 = __pyx_v_n;
*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_lcn_view.data + __pyx_t_27 * __pyx_v_img_lcn_view.strides[0]) ) + __pyx_t_28 * __pyx_v_img_lcn_view.strides[1]) )) = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_25 * __pyx_v_img.strides[0]) ) + __pyx_t_26 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) / (__pyx_v_stddev + __pyx_v_eps));
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">55</span>: <span class="n">img_std_view</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">stddev</span></pre>
<pre class='cython code score-0 '> __pyx_t_29 = __pyx_v_m;
</pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">55</span>: <span class="n">img_std_view</span><span class="p">[</span><span
class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span
class="n">stddev</span></pre>
<pre class='cython code score-0 '> __pyx_t_29 = __pyx_v_m;
__pyx_t_30 = __pyx_v_n;
*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_std_view.data + __pyx_t_29 * __pyx_v_img_std_view.strides[0]) ) + __pyx_t_30 * __pyx_v_img_std_view.strides[1]) )) = __pyx_v_stddev;
}
}
</pre><pre class="cython line score-0">&#xA0;<span class="">56</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">57</span>: <span class="c"># return both</span></pre>
<pre class="cython line score-10" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">58</span>: <span class="k">return</span> <span class="n">img_lcn</span><span class="p">,</span> <span class="n">img_std</span></pre>
<pre class='cython code score-10 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);
</pre>
<pre class="cython line score-0">&#xA0;<span class="">56</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">57</span>: <span class="c"># return both</span></pre>
<pre class="cython line score-10"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">58</span>: <span class="k">return</span> <span class="n">img_lcn</span><span class="p">,</span> <span
class="n">img_std</span></pre>
<pre class='cython code score-10 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);
__pyx_t_1 = <span class='py_c_api'>PyTuple_New</span>(2);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 58, __pyx_L1_error)</span>
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
<span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_v_img_lcn);
@@ -717,4 +903,7 @@ static PyObject *__pyx_pf_3lcn_normalize(CYTHON_UNUSED PyObject *__pyx_self, __P
__pyx_r = __pyx_t_1;
__pyx_t_1 = 0;
goto __pyx_L0;
</pre></div></body></html>
</pre>
</div>
</body>
</html>
data/lcn/setup.py
+1 -1
View File
@@ -2,5 +2,5 @@ from distutils.core import setup
from Cython.Build import cythonize
setup(
ext_modules = cythonize("lcn.pyx",annotate=True)
ext_modules=cythonize("lcn.pyx", annotate=True)
)
data/lcn/test_lcn.py
+13 -13
View File
@@ -5,43 +5,43 @@ from scipy import misc
# load and convert to float
img = misc.imread('img.png')
img = img.astype(np.float32)/255.0
img = img.astype(np.float32) / 255.0
# normalize
img_lcn, img_std = lcn.normalize(img,5,0.05)
img_lcn, img_std = lcn.normalize(img, 5, 0.05)
# normalize to reasonable range between 0 and 1
#img_lcn = img_lcn/3.0
#img_lcn = np.maximum(img_lcn,0.0)
#img_lcn = np.minimum(img_lcn,1.0)
# img_lcn = img_lcn/3.0
# img_lcn = np.maximum(img_lcn,0.0)
# img_lcn = np.minimum(img_lcn,1.0)
# save to file
#misc.imsave('lcn2.png',img_lcn)
# misc.imsave('lcn2.png',img_lcn)
print ("Orig Image: %d x %d (%s), Min: %f, Max: %f" % \
(img.shape[0], img.shape[1], img.dtype, img.min(), img.max()))
print ("Norm Image: %d x %d (%s), Min: %f, Max: %f" % \
(img_lcn.shape[0], img_lcn.shape[1], img_lcn.dtype, img_lcn.min(), img_lcn.max()))
print("Orig Image: %d x %d (%s), Min: %f, Max: %f" % \
(img.shape[0], img.shape[1], img.dtype, img.min(), img.max()))
print("Norm Image: %d x %d (%s), Min: %f, Max: %f" % \
(img_lcn.shape[0], img_lcn.shape[1], img_lcn.dtype, img_lcn.min(), img_lcn.max()))
# plot original image
plt.figure(1)
img_plot = plt.imshow(img)
img_plot.set_cmap('gray')
plt.clim(0, 1) # fix range
plt.clim(0, 1) # fix range
plt.tight_layout()
# plot normalized image
plt.figure(2)
img_lcn_plot = plt.imshow(img_lcn)
img_lcn_plot.set_cmap('gray')
#plt.clim(0, 1) # fix range
# plt.clim(0, 1) # fix range
plt.tight_layout()
# plot stddev image
plt.figure(3)
img_std_plot = plt.imshow(img_std)
img_std_plot.set_cmap('gray')
#plt.clim(0, 0.1) # fix range
# plt.clim(0, 0.1) # fix range
plt.tight_layout()
plt.show()