def imgLoad(imgPath, color=True):
"""
Load an image converting from grayscale or alpha as needed.
Input
imgPath - image path
color - flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
Output
image - an image with type np.float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
"""
# load
try:
img0 = skimage.io.imread(imgPath)
img = skimage.img_as_float(img0).astype(np.float32)
except:
pr('unable to open img: {}'.format(imgPath))
return None
# color channel
if img.ndim == 2:
img = img[:, :, np.newaxis]
if color:
img = np.tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img
def equal(nm, A, B):
"""
Check whether two matrix are equal or not.
Input
nm - name
A - matrix 1
B - matrix 2
Output
isEq - result, True | False
"""
# check type
if not A.__class__ == B.__class__:
pri.pr('{}, different types: {} vs {}'.format(nm, A.__class__, B.__class__))
return False
# dictionary, check each key
if A.__class__ == dict:
keyAs = A.keys()
keyBs = A.keys()
keyAs.sort()
keyBs.sort()
if not equal(nm, keyAs, keyBs):
return False
for key in keyAs:
if not equal(nm, A[key], B[key]):
return False
return True
# list
if A.__class__ == list:
if not len(A) == len(B):
pri.pr('{}, different lens: {} vs {}'.format(nm, len(A), len(B)))
return False
for i in range(len(A)):
if not equal(nm, A[i], B[i]):
return False
return True
# string
if A.__class__ == str:
if not A == B:
pri.pr('{}, different strings: {} vs {}'.format(nm, A, B))
return False
return True
# check dimension
if not A.shape == B.shape:
pri.pr('{}, different size: {} vs {}'.format(nm, A.shape, B.shape))
return False
# check value
diff = max(abs((A - B).flatten()))
isEq = diff < 1e-5
if isEq:
pri.pr('{}, same'.format(nm))
else:
pri.pr('{}, different values: diff = {}'.format(nm, diff))
return isEq