def save_img(path,
x,
data_format=None,
file_format=None,
scale=True,
**kwargs):
"""Saves an image stored as a Numpy array to a path or file object.
Arguments:
path: Path or file object.
x: Numpy array.
data_format: Image data format,
either "channels_first" or "channels_last".
file_format: Optional file format override. If omitted, the
format to use is determined from the filename extension.
If a file object was used instead of a filename, this
parameter should always be used.
scale: Whether to rescale image values to be within `[0, 255]`.
**kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
"""
if data_format is None:
data_format = backend.image_data_format()
image.save_img(path,
x,
data_format=data_format,
file_format=file_format,
scale=scale, **kwargs)
def save_img(path,
x,
data_format=None,
file_format=None,
scale=True,
**kwargs):
"""Saves an image stored as a Numpy array to a path or file object.
Arguments:
path: Path or file object.
x: Numpy array.
data_format: Image data format,
either "channels_first" or "channels_last".
file_format: Optional file format override. If omitted, the
format to use is determined from the filename extension.
If a file object was used instead of a filename, this
parameter should always be used.
scale: Whether to rescale image values to be within `[0, 255]`.
**kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
"""
if data_format is None:
data_format = backend.image_data_format()
image.save_img(path,
x,
data_format=data_format,
file_format=file_format,
scale=scale,
**kwargs)
def save_image(image_npy, dest_folder_path=os.getcwd(), dest_filename="img.bmp"):
if image_npy is None:
raise Exception("The provided numpy Array is None")
if not len(image_npy.shape) in (2, 3):
raise Exception("The Number od dimensions of the provided image is neither 2 nor 3.")
if not os.path.isdir(dest_folder_path):
raise Exception("The provided folder path is not existent")
if dest_filename in os.listdir(dest_folder_path):
raise Exception("A file with the same name is already present in this folder")
pil_img = array_to_img(image_npy)
save_img(os.path.join(dest_folder_path, dest_filename), pil_img)
def data_augment():
root_dir = os.path.join(DATA_PATH, "Image")
for parents, dirnames, filenames in os.walk(root_dir):
if parents != root_dir:
#print("parents = ", parents)
#print("dirnames = ", dirnames)
#print("flienames = ", filenames)
name_store = filenames
#print("name_store = ", name_store)
new_name_store = []
for i in range(
(factor - 1) *
len(name_store)): #一共要生成factor * len(name_store)个新名字
rand_num = np.random.randint(0, len(name_store))
ext = os.path.splitext(name_store[rand_num])[-1]
src = os.path.join(parents, name_store[rand_num])
new_name = str(np.random.randint(
10000, 100000)) + ext #新名字的范围在10000～99999
for j in range(len(new_name_store)): #找到一个独一无二的新名字
if new_name == new_name_store[j]: #如果遇到重复的名字
new_name = str(np.random.randint(
10000, 1000000)) + ext #生成新名字
j = 0 #再次遍历new_name_store
new_name_store.append(new_name)
dst = os.path.join(parents, new_name)
datagen = ImageDataGenerator(rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest")
img = image.load_img(src)
x = image.img_to_array(img)
img_width = x.shape[0]
img_height = x.shape[1]
x = x.reshape((1, ) + x.shape)
for batch in datagen.flow(x, batch_size=16):
image.save_img(dst,
batch.reshape(img_width, img_height, 3),
file_format=ext[1:])
print("Add: ", dst)
break
##开始更改csv文件
change_csv(os.path.join("Image",
os.path.split(parents)[-1]), new_name)
def stitch_images_and_save(filepath_list: list, stitched_folder: str,
split_locations: tuple = IMAGE_SPLIT_LOCATIONS):
print("\n\nNew Stitch:")
if not os.path.isdir(stitched_folder):
os.makedirs(stitched_folder)
if re.search("Alge", filepath_list[0]):
stitched_filename = "stitched_Alge"
elif re.search("Zwiebelzelle", filepath_list[0]):
stitched_filename = "stitched_Zwiebelzelle"
else:
stitched_filename = "stitched"
split_images_list = []
for filepath in filepath_list:
print(filepath)
split_images_list.append(split_image(filepath, split_locations))
filename = filename_from_path(filepath)
stitched_filename += "_" + filename.split(".")[0][8:]
stitched_filename += ".bmp"
stitched_filepath = os.path.join(stitched_folder, stitched_filename)
rearranged_images = [[], [], [], []] # [image3, image4, image2, image1]
for tup in split_images_list:
if tup[1] == 1:
rearranged_images[3] = tup[0]
elif tup[1] == 2:
rearranged_images[2] = tup[0]
elif tup[1] == 3:
rearranged_images[0] = tup[0]
elif tup[1] == 4:
rearranged_images[1] = tup[0]
else:
print(f"{Fore.RED}[ERROR] Wrong image group found. "
f"The image group should be '1' or '2' or '3' or '4'{Style.RESET_ALL}")
sys.exit(-2)
merged_img1 = np.concatenate((rearranged_images[0], rearranged_images[1]), axis=1)
merged_img2 = np.concatenate((rearranged_images[2], rearranged_images[3]), axis=1)
img_height1, img_height2 = merged_img1.shape[0], merged_img2.shape[0]
i, j = split_locations[8], split_locations[9]
split_merged_img1 = (np.array_split(merged_img1, [img_height1-i], axis=0))[0]
split_merged_img2 = (np.array_split(merged_img2, [220], axis=0))[1]
merged_total = np.concatenate((split_merged_img1, split_merged_img2), axis=0)
save_img(stitched_filepath, merged_total)
def save_img(path,
x,
data_format=None,
file_format=None,
scale=True, **kwargs):
if data_format is None:
data_format = backend.image_data_format()
return image.save_img(path,
x,
data_format=data_format,
file_format=file_format,
scale=scale, **kwargs)
def save_img(path,
x,
data_format=None,
file_format=None,
scale=True, **kwargs):
if data_format is None:
data_format = backend.image_data_format()
return image.save_img(path,
x,
data_format=data_format,
file_format=file_format,
scale=scale, **kwargs)
def predict(path_to_image: str):
# load the model we saved
model = MnistCnnModel(num_classes, (img_width, img_height), 3,
'./final.h5').get_trained_model()
img = image.load_img(path_to_image,
target_size=(img_height, img_width),
color_mode='rgba')
img.load()
background = Image.new('RGBA', (72, 72), (71, 113, 77, 255))
background.paste(img, mask=img.split()[3]) # 3 is the alpha channel
image.save_img('./temp.png', background, file_format='png')
# background.convert('RGB') - doesn't work here but works in load_img
x = image.load_img('./temp.png', color_mode='rgb')
x = image.img_to_array(x)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_classes(images, batch_size=1)
# print the classes, the images belong to
with open(settings.JSON_CLASSES, 'r') as f:
classes_dict = ujson.load(f)
print(f'classes is: {classes_dict[str(classes[0])]}')
def main():
# sym, arg_params, aux_params = mx.model.load_checkpoint("base_net", 0)
# net.load_parameters('testnet.params')
save_img()
K.function()
pass
def saveResult(save_path, npyfile):
for i, item in enumerate(npyfile):
#img = item[:, :, 1]
save_img(os.path.join(save_path, "%d_predict.png" % i), item)
