I'm trying to make a numpy with this shape(number_of_images,32,32) all the images have the same dimensions: 32,32 here is my code:
data=np.array([])
for filename in glob.glob(path+'*.tif'):
im = np.array([np.array(cv2.imread(filename, cv2.IMREAD_GRAYSCALE))])
#im = preprocess(im)
im = NormalizeData(im)
data=np.append(data,im)
im shape is (1,32,32). however data shape is not the one I wanted (number_of_images,32,32). the one I currently have is (113664,)
try using lists and at the end cast to numpy - less confusing
def main():
cv_img_fake = np.zeros(shape=(1, 32, 32), dtype=np.uint8)
print('image shape {}'.format(cv_img_fake.shape))
images = []
for filename_i in range(10): # imagine 10 images
print('filename {}:'.format(filename_i))
im = cv_img_fake.copy() # shape 1,32,32
print('\timage shape {}'.format(im.shape))
im = im.reshape(-1, im.shape[-1]) # shape 32,32
print('\timage shape {}'.format(im.shape))
# do to im whatever you want except changing the dims
# check after every function by printing dims didn't change - e.g. still 32,32
# im = NormalizeData(im)
# print('\timage shape {}'.format(im.shape))
# im = preprocess(im)
# print('\timage shape {}'.format(im.shape))
images.append(im)
images = np.uint8(images) # 10 images of 32, 32
print('images shape {}'.format(images.shape)) # 10,32,32
return
Output:
image shape (1, 32, 32)
filename 0:
image shape (1, 32, 32)
image shape (32, 32)
filename 1:
image shape (1, 32, 32)
image shape (32, 32)
filename 2:
image shape (1, 32, 32)
image shape (32, 32)
filename 3:
image shape (1, 32, 32)
image shape (32, 32)
filename 4:
image shape (1, 32, 32)
image shape (32, 32)
filename 5:
image shape (1, 32, 32)
image shape (32, 32)
filename 6:
image shape (1, 32, 32)
image shape (32, 32)
filename 7:
image shape (1, 32, 32)
image shape (32, 32)
filename 8:
image shape (1, 32, 32)
image shape (32, 32)
filename 9:
image shape (1, 32, 32)
image shape (32, 32)
images shape (10, 32, 32)
(113664,)desired? how do you figure that? the number equals 111 * 32 * 32 so you have 111 images?