def main():
'''
????????
'''
im_array = ndimage.imread("rwby.bmp", mode='RGB')
print(len(im_array), len(im_array))
color = set()
for i in im_array:
for j in i:
color.add(tuple(j))
print('{')
for i in color:
print("\"{0}\":,".format(i))
print('}')
# for noi,i in enumerate(im_array):
# for noj,j in enumerate(i):
# print("Row:%d Col:%d color: %s" %(noi, noj, j))
python类imread()的实例源码
def _generate_batch(self, meta):
image = ndimage.imread(meta.image_path)
height, width, _ = meta.shape
if height > width:
scale = self._image_scale_size / width
else:
scale = self._image_scale_size / height
# TODO: the dimensions in caffe is (batch elem, channel, height, width)
resized_image = ndimage.zoom(image, (scale, scale, 1))
bboxes = np.empty((len(meta.objects), 5))
for i, obj in enumerate(meta.objects):
bboxes[i][:4] = obj['bbox']
bboxes[i][4] = obj['class_index']
return np.expand_dims(resized_image, 0), scale, bboxes
def read_labeled_image_list(data_dir, data_list):
"""Reads txt file containing paths to images and ground truth masks.
Args:
data_dir: path to the directory with images and masks.
data_list: path to the file with lines of the form '/path/to/image /path/to/mask'.
Returns:
Two lists with all file names for images and masks, respectively.
"""
f = open(data_list, 'r')
images = []
masks = []
shape = []
for line in f:
image, mask = line.strip("\n").split(' ')
images.append(data_dir + image)
shape.append(ndimage.imread(data_dir + image).shape[:2])
masks.append(data_dir + mask)
return images, masks, shape
def demo(stem):
flist = getimgfiles(stem)
ext = flist[0].suffix
for i in range(len(flist)-1):
fn1 = f'{stem}.{i}{ext}'
im1 = imread(fn1,flatten=True).astype(float) #flatten=True is rgb2gray
# Iold = gaussian_filter(Iold,FILTER)
fn2 = f'{stem}.{i+1}{ext}'
im2 = imread(fn2,flatten=True).astype(float)
# Inew = gaussian_filter(Inew,FILTER)
U,V = HornSchunck(im1, im2, 1., 100)
compareGraphs(U,V, im2)
return U,V
def demo(stem, kernel=5,Nfilter=7):
flist = getimgfiles(stem)
ext = flist[0].suffix
#%% priming read
im1 = imread(f'{stem}.0{ext}', flatten=True)
Y,X = im1.shape
#%% evaluate the first frame's POI
POI = getPOI(X,Y,kernel)
#% get the weights
W = gaussianWeight(kernel)
#%% loop over all images in directory
for i in range(1,len(flist)):
im2 = imread(f'{stem}.{i}{ext}', flatten=True)
im2 = gaussian_filter(im2, Nfilter)
V = LucasKanade(im1, im2, POI, W, kernel)
compareGraphsLK(im1, im2, POI, V)
im1 = im2
def omniglot_folder_to_NDarray(path_im):
alphbts = os.listdir(path_im)
ALL_IMGS = []
for alphbt in alphbts:
chars = os.listdir(os.path.join(path_im, alphbt))
for char in chars:
img_filenames = os.listdir(os.path.join(path_im, alphbt, char))
char_imgs = []
for img_fn in img_filenames:
fn = os.path.join(path_im, alphbt, char, img_fn)
I = imread(fn)
I = np.invert(I)
char_imgs.append(I)
ALL_IMGS.append(char_imgs)
return np.array(ALL_IMGS)
def load_img_as_points(filename):
# Load image file and return coordinates of black pixels in the binary image
#
# Input
# filename : string, absolute path to image
#
# Output:
# D : [n x 2] rows are coordinates
#
I = imread(filename, flatten=True)
# Convert to boolean array and invert the pixel values
I = ~np.array(I, dtype=np.bool)
# Create a new array of all the non-zero element coordinates
D = np.array(I.nonzero()).T
return D - D.mean(axis=0)
# Main function
def get_rgbd_file(self, dirname, offset):
associations = self.seq_dir_map[dirname]['associations']
if associations[offset, 1].startswith('depth'):
rgb_filename = os.path.join(dirname, associations[offset, 3])
depth_filename = os.path.join(dirname, associations[offset, 1])
else:
rgb_filename = os.path.join(dirname, associations[offset, 1])
depth_filename = os.path.join(dirname, associations[offset, 3])
rgb_img = ndimage.imread(rgb_filename)
depth_img = ndimage.imread(depth_filename)
width = height = 224
# Reshape
depth_img = np.reshape(depth_img, list(depth_img.shape) + [1])
depth_img = 255 * depth_img / np.max(depth_img)
rgbd_img = np.concatenate((rgb_img, depth_img), 2)
# Resize
rgbd_img = transform.resize(rgbd_img, [width, height], preserve_range=True)
return rgb_filename, depth_filename, rgbd_img.astype(np.float32)
def read_rgb_image(filepath):
rgb_img = ndimage.imread(filepath)
width = height = 224
img_width = rgb_img.shape[1]
img_height = rgb_img.shape[0]
# scale such that smaller dimension is 256
if img_width < img_height:
factor = 256.0 / img_width
else:
factor = 256.0 / img_height
rgb_img = transform.rescale(rgb_img, factor, preserve_range=True)
# crop randomly
width_start = np.random.randint(0, rgb_img.shape[1] - width)
height_start = np.random.randint(0, rgb_img.shape[0] - height)
rgb_img = rgb_img[height_start:height_start + height, width_start:width_start + width]
return rgb_img
def rotate_save(path):
filepath = os.path.join(os.getcwd(),path)
images_files = os.listdir(filepath)
num_img = 0
for i,image in enumerate(images_files):
image_file = os.path.join(filepath,image)
try:
img = ndimage.imread(image_file)
img = img[:,:,0]
new_im = img.fromarray(img)
for j in range(0,360,72):
im = new_im.rotate(j)
filename = "%s%05d.png"%(path,num_img)
im.save(filename)
num_img+=1
except Exception as e:
print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
def load_next_image(self):
"""
Loads next image from train index for training.
:return: True if the next image is present, else False
"""
if len(self.image_list) == self.image_ptr:
return False
sys.stderr.write('Loaded Image #' + str(self.image_ptr) + ' ...\n')
self.image = ndimage.imread(self.image_list[self.image_ptr])
is_color = self.__check_color()
if is_color:
self.image = rgb2gray(self.image)
assert self.image.shape == (256, 256), 'Image not 256 x 256'
self.__break_into_jigzaw_pieces()
self.image_ptr += 1
self.tries = 1
return True
def load_next_image(self):
"""
Loads next image from train index for training.
:return: True if the next image is present, else False
"""
if len(self.image_list) == self.image_ptr:
return False
print 'Loaded New Image'
self.image = ndimage.imread(self.image_list[self.image_ptr])
self.image_name = self.image_list[self.image_ptr]
is_color = self.__check_color()
if is_color:
self.image = rgb2gray(self.image)
assert self.image.shape == (256, 256), 'Image not 256 x 256'
self.image_ptr += 1
return True
def getMeanVar(self):
depths = None
for f in self.depthFiles:
depthImg = imread(f).astype(np.float32)/256
validDepths = depthImg[np.nonzero(depthImg != 0)]
if(depths == None):
depths = validDepths
else:
depths = np.concatenate((depths, validDepths))
self.mean = np.mean(depths)
self.std = np.std(depths)
print "depth mean: ", self.mean
print "depth std: ", self.std
#Function to return new image and depth file
#TODO generate random ranking and randomize images
def crawl_directory(directory, augment_with_rotations=False, first_label=0):
"""Crawls data directory and returns stuff."""
label_idx = first_label
images = []
labels = []
info = []
# traverse root directory
for root, _, files in os.walk(directory):
logging.info('Reading files from %s', root)
for file_name in files:
full_file_name = os.path.join(root, file_name)
img = imread(full_file_name, flatten=True)
for idx, angle in enumerate([0, 90, 180, 270]):
if not augment_with_rotations and idx > 0:
break
images.append(imrotate(img, angle))
labels.append(label_idx + idx)
info.append(full_file_name)
if len(files) == 20:
label_idx += 4 if augment_with_rotations else 1
return images, labels, info
def read(self, file_name=None):
if file_name != None:
self.file_name = file_name
elif self.file_name != None:
pass
else:
raise Exception(" %s , Undefined file name: " %
sys._getframe().f_code.co_name)
img_rgb = Image(color_depth=8)
self.raw = ndimage.imread(self.file_name)
img_rgb.image = np.array(self.raw, dtype=img_rgb.dtype)
return img_rgb
def read(self, file_name=None):
if file_name != None:
self.file_name = file_name
elif self.file_name != None:
pass
else:
raise Exception(" %s , Undefined file name: " %
sys._getframe().f_code.co_name)
rgb = Image(color_depth=16)
raw = None
with rawpy.imread(self.file_name) as raw:
self.raw = raw.postprocess(output_bps=16)
rgb.image = np.array(self.raw, dtype=rgb.dtype)
return rgb
def read_data(self, train_split=0.80, dev_split=0.10, test_split=0.10):
"""
Class function to read images from `self.image_dir`and split them into three groups: train/dev/test.
"""
assert (train_split + dev_split + test_split == 1.0)
all_images = glob.glob(self.image_dir + "*.png")
data = []
for image_path in all_images:
image = imread(image_path, flatten=True)
image = image.reshape(IMAGE_WIDTH*IMAGE_HEIGHT)
# image = np.multiply(image, 1.0 / 255.0) No scaling here
data.append(image)
data = np.array(data)
data = data.astype(np.uint8)
total_images = data.shape[0]
train_limit = int(total_images * train_split)
dev_limit = train_limit + int(total_images * dev_split)
self.train = data[:train_limit]
self.dev = data[train_limit:dev_limit]
self.test = data[dev_limit:]
# Only shuffling training data.
random.shuffle(self.train)
self.data_dict = {
'train': self.train,
'dev': self.dev,
'test': self.test}
def main():
'''
????????
'''
im_array = ndimage.imread("greytech.png", mode='RGB')
print(len(im_array), len(im_array[0]))
color = set()
for i in im_array:
for j in i:
color.add(tuple(j))
# tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))]
#
# for i in range((len(im_array))):
# for j in range(len(im_array[0])):
# print(str(tuple(im_array[i][j])))
# if str(tuple(im_array[i][j]))!= "(255, 255, 255)":
# tmp[i][j]=(0,0,0)
# else:
# tmp[i][j]=im_array[i][j]
#
# misc.imsave("test.bmp", tmp)
print('{')
for i in color:
print("\"{0}\":,".format(i))
print('}')
# for noi,i in enumerate(im_array):
# for noj,j in enumerate(i):
# print("Row:%d Col:%d color: %s" %(noi, noj, j))
def main():
'''
????????
'''
im_array = ndimage.imread("ustc.bmp", mode='RGB')
print(len(im_array), len(im_array))
color = set()
for i in im_array:
for j in i:
color.add(tuple(j))
# tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))]
#
# for i in range((len(im_array))):
# for j in range(len(im_array[0])):
# print(str(tuple(im_array[i][j])))
# if str(tuple(im_array[i][j]))!= "(255, 255, 255)":
# tmp[i][j]=(0,0,0)
# else:
# tmp[i][j]=im_array[i][j]
#
# misc.imsave("test.bmp", tmp)
print('{')
for i in color:
print("\"{0}\":,".format(i))
print('}')
# for noi,i in enumerate(im_array):
# for noj,j in enumerate(i):
# print("Row:%d Col:%d color: %s" %(noi, noj, j))
def main():
'''
????????
'''
im_array = ndimage.imread("greytech.png", mode='RGB')
print(len(im_array), len(im_array[0]))
color = set()
for i in im_array:
for j in i:
color.add(tuple(j))
# tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))]
#
# for i in range((len(im_array))):
# for j in range(len(im_array[0])):
# print(str(tuple(im_array[i][j])))
# if str(tuple(im_array[i][j]))!= "(255, 255, 255)":
# tmp[i][j]=(0,0,0)
# else:
# tmp[i][j]=im_array[i][j]
#
# misc.imsave("test.bmp", tmp)
print('{')
for i in color:
print("\"{0}\":,".format(i))
print('}')
# for noi,i in enumerate(im_array):
# for noj,j in enumerate(i):
# print("Row:%d Col:%d color: %s" %(noi, noj, j))
def main():
'''
????????
'''
im_array = ndimage.imread("ms.bmp", mode='RGB')
print(len(im_array), len(im_array))
color = set()
for i in im_array:
for j in i:
color.add(tuple(j))
# tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))]
#
# for i in range((len(im_array))):
# for j in range(len(im_array[0])):
# print(str(tuple(im_array[i][j])))
# if str(tuple(im_array[i][j]))!= "(255, 255, 255)":
# tmp[i][j]=(0,0,0)
# else:
# tmp[i][j]=im_array[i][j]
#
# misc.imsave("test.bmp", tmp)
print('{')
for i in color:
print("\"{0}\":,".format(i))
print('}')
# for noi,i in enumerate(im_array):
# for noj,j in enumerate(i):
# print("Row:%d Col:%d color: %s" %(noi, noj, j))
def load_logo(data_dir):
image_files = os.listdir(data_dir)
dataset = np.ndarray(
shape=(len(image_files), CNN_IN_HEIGHT, CNN_IN_WIDTH, CNN_IN_CH),
dtype=np.float32)
print(data_dir)
num_images = 0
for image in image_files:
image_file = os.path.join(data_dir, image)
try:
image_data = (ndimage.imread(image_file).astype(float) -
PIXEL_DEPTH / 2) / PIXEL_DEPTH
if image_data.shape != (CNN_IN_HEIGHT, CNN_IN_WIDTH, CNN_IN_CH):
raise Exception('Unexpected image shape: %s' %
str(image_data.shape))
dataset[num_images, :, :] = image_data
num_images = num_images + 1
except IOError as e:
print('Could not read:', image_file, ':', e,
'-it\'s ok, skipping.')
dataset = dataset[0:num_images, :, :]
print('Full dataset tensor:', dataset.shape)
print('Mean:', np.mean(dataset))
print('Standard deviation:', np.std(dataset))
return dataset
def reshape_images(cls, source_folder, target_folder, height=128, width=128,
extensions=('.jpg', '.jpeg', '.png')):
""" copy images and reshape them"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Resizing '", source_folder, "' images...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.reshape_images(source_folder + '/' + filename,
target_folder + '/' + filename,
height, width, extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
image_resized = misc.imresize(image, (height, width))
misc.imsave(target_folder + "/" + filename, image_resized)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
image_resized = misc.imresize(image, (height, width))
misc.imsave(target_folder + "/" + filename, image_resized)
def crop_images(cls, source_folder, target_folder, height=128, width=128,
extensions=('.jpg', '.jpeg', '.png')):
""" copy images and center crop them"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and crop:
print("Cropping '", source_folder, "' images...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.crop_images(source_folder + '/' + filename,
target_folder + '/' + filename,
height, width, extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
[width_original, height_original, _] = image.shape
offset_w = (width_original - width) / 2
offset_h = (width_original - width) / 2
image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :]
misc.imsave(target_folder + "/" + filename, image_cropped)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, mode="RGB")
[width_original, height_original, _] = image.shape
offset_w = (width_original - width) / 2
offset_h = (width_original - width) / 2
image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :]
misc.imsave(target_folder + "/" + filename, image_cropped)
def convert_to_grayscale(cls, source_folder, target_folder,
extensions=('.jpg', '.jpeg', '.png')):
""" convert images from RGB to Grayscale"""
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Convert '", source_folder, "' images to grayscale...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.convert_to_grayscale(source_folder + '/' + filename,
target_folder + '/' + filename,
extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, flatten=True)
misc.imsave(target_folder + "/" + filename, image)
else:
for extension in extensions:
if filename.endswith(extension):
copy2(source_folder + "/" + filename,
target_folder + "/" + filename)
image = ndimage.imread(target_folder + "/" + filename, flatten=True)
misc.imsave(target_folder + "/" + filename, image)
def convert_format(cls, source_folder, target_folder,
extensions=('.jpg', '.jpeg', '.png'), new_extension='.jpg'):
""" change images from one format to another (eg. change png files to jpeg) """
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and reshape:
print("Change format of '", source_folder, "' files...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.convert_format(source_folder + '/' + filename,
target_folder + '/' + filename,
extensions=extensions, new_extension=new_extension)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
copy2(source_folder + "/" + filename,
target_folder + "/" + filename + new_extension)
image = ndimage.imread(target_folder + "/" + filename + new_extension)
misc.imsave(target_folder + "/" + filename + new_extension, image)
else:
for extension in extensions:
if filename.endswith(extension):
new_filename = os.path.splitext(filename)[0] + new_extension
copy2(source_folder + "/" + filename,
target_folder + "/" + new_filename)
image = ndimage.imread(target_folder + "/" + new_filename)
misc.imsave(target_folder + "/" + new_filename, image)
def convert_to_array(cls, source_folder, target_folder, create_labels_file=False,
flatten=False, extensions=('.jpg', '.jpeg', '.png')):
""" Read all images in subfolders and convert them to a single array """
# check source_folder and target_folder:
cls.check_folder_existance(source_folder, throw_error_if_no_folder=True)
cls.check_folder_existance(target_folder, display_msg=False)
if source_folder[-1] == "/":
source_folder = source_folder[:-1]
if target_folder[-1] == "/":
target_folder = target_folder[:-1]
# read images and concatenate:
print("Converting '", source_folder, "' images...")
for filename in os.listdir(source_folder):
if os.path.isdir(source_folder + '/' + filename):
cls.convert_to_array(source_folder + '/' + filename, target_folder,
create_labels_file=create_labels_file, extensions=extensions)
else:
if extensions == '' and os.path.splitext(filename)[1] == '':
image = ndimage.imread(source_folder + "/" + filename, mode="RGB")
if (flatten):
cls.data.append(image.flatten())
else:
cls.data.append(image)
if create_labels_file:
cls.labels.append(source_folder.replace('/', '_'))
else:
for extension in extensions:
if filename.endswith(extension):
image = ndimage.imread(source_folder + "/" + filename, mode="RGB")
if (flatten):
cls.data.append(image.flatten())
else:
cls.data.append(image)
if create_labels_file:
cls.labels.append(source_folder.replace('/', '_'))
def load_flow_from_png(png_path):
return(imread(png_path)[:,:,0:2].astype(float) - 128)
def KITTI_loader(root,path_imgs, path_flo):
imgs = [os.path.join(root,path) for path in path_imgs]
flo = os.path.join(root,path_flo)
return [imread(img) for img in imgs],load_flow_from_png(flo)
def default_loader(root, path_imgs, path_flo):
imgs = [os.path.join(root,path) for path in path_imgs]
flo = os.path.join(root,path_flo)
return [imread(img).astype(np.float32) for img in imgs],load_flo(flo)
