Python Image.open方法代码示例

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def loop():

    print('Looping through all images in folder {}\n'
          'CRL+C to skip image'.format(folder_path))

    try:

        for img_file in os.listdir(folder_path):

            if img_file.endswith(icon_extension):

                print('Drawing image: {}'.format(folder_path + img_file))

                img = Image.open(folder_path + img_file)

                draw_animation(img)

            else:

                print('Not using this file, might be not an image: {}'.format(img_file))

    except KeyboardInterrupt:
        unicorn.off()

    unicorn.off() 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def weather_icons():
    try:

        if argv[1] == 'loop':

            loop()

        elif argv[1] in os.listdir(folder_path):

            print('Drawing Image: {}'.format(argv[1]))

            img = Image.open(folder_path + argv[1])

            draw_animation(img)
            unicorn.off()

        else:
            help()

    except IndexError:
        help() 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def screenshot(self, name):
        '''
        screenshot()
        Takes a screenshot of the browser
        '''
        if do_crop:
            print('cropping screenshot')
            #  Grab screenshot rather than saving
            im = self.browser.get_screenshot_as_png()
            im = Image.open(BytesIO(im))

            #  Crop to specifications
            im = im.crop((crop_x, crop_y, crop_width, crop_height))
            im.save(name)
        else:
            self.browser.save_screenshot(name)
        print("success saving screenshot: %s" % name)
        return name 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def output_coords():
    # Open the file to output the co-ordinates to
    f1 = open('./setup_data.py', 'w+')

    # Print the dictionary data to the file
    print >>f1, 'boxes = ['
    
    for i in range(Boxes.length()):
        c = Boxes.get(i).get_output(SelWindow.bgcoords)
        
        if c != None:
            o = (i)
            print >>f1, c, ','

    print >>f1, ']'
    print 'INFO: Box data saved in file boxdata.py.'
    tkMessageBox.showinfo("Pi Setup", "Box data saved in file.")

# -----------------------------------------------------------------------------
# Main Program
# ----------------------------------------------------------------------------- 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def __init__(self, master=None):
        tk.Frame.__init__(self, master)
        self.master = master
        self.init_window()
        
        self.about_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/LPHK-banner.png"))
        self.info_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/info.png"))
        self.warning_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/warning.png"))
        self.error_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/error.png"))
        self.alert_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/alert.png"))
        self.scare_image = ImageTk.PhotoImage(Image.open(PATH + "/resources/scare.png"))
        self.grid_drawn = False
        self.grid_rects = [[None for y in range(9)] for x in range(9)]
        self.button_mode = "edit"
        self.last_clicked = None
        self.outline_box = None 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def download_image(image_id, url, x1, y1, x2, y2, output_dir):
    """Downloads one image, crops it, resizes it and saves it locally."""
    output_filename = os.path.join(output_dir, image_id + '.png')
    if os.path.exists(output_filename):
        # Don't download image if it's already there
        return True
    try:
        # Download image
        url_file = urlopen(url)
        if url_file.getcode() != 200:
            return False
        image_buffer = url_file.read()
        # Crop, resize and save image
        image = Image.open(BytesIO(image_buffer)).convert('RGB')
        w = image.size[0]
        h = image.size[1]
        image = image.crop((int(x1 * w), int(y1 * h), int(x2 * w),
                            int(y2 * h)))
        image = image.resize((299, 299), resample=Image.ANTIALIAS)
        image.save(output_filename)
    except IOError:
        return False
    return True 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def _prepare_sample_data(self, submission_type):
    """Prepares sample data for the submission.

    Args:
      submission_type: type of the submission.
    """
    # write images
    images = np.random.randint(0, 256,
                               size=[BATCH_SIZE, 299, 299, 3], dtype=np.uint8)
    for i in range(BATCH_SIZE):
      Image.fromarray(images[i, :, :, :]).save(
          os.path.join(self._sample_input_dir, IMAGE_NAME_PATTERN.format(i)))
    # write target class for targeted attacks
    if submission_type == 'targeted_attack':
      target_classes = np.random.randint(1, 1001, size=[BATCH_SIZE])
      target_class_filename = os.path.join(self._sample_input_dir,
                                           'target_class.csv')
      with open(target_class_filename, 'w') as f:
        for i in range(BATCH_SIZE):
          f.write((IMAGE_NAME_PATTERN + ',{1}\n').format(i, target_classes[i])) 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def _load_dataset_clipping(self, dataset_dir, epsilon):
    """Helper method which loads dataset and determines clipping range.

    Args:
      dataset_dir: location of the dataset.
      epsilon: maximum allowed size of adversarial perturbation.
    """
    self.dataset_max_clip = {}
    self.dataset_min_clip = {}
    self._dataset_image_count = 0
    for fname in os.listdir(dataset_dir):
      if not fname.endswith('.png'):
        continue
      image_id = fname[:-4]
      image = np.array(
          Image.open(os.path.join(dataset_dir, fname)).convert('RGB'))
      image = image.astype('int32')
      self._dataset_image_count += 1
      self.dataset_max_clip[image_id] = np.clip(image + epsilon,
                                                0,
                                                255).astype('uint8')
      self.dataset_min_clip[image_id] = np.clip(image - epsilon,
                                                0,
                                                255).astype('uint8') 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def __init__(self, filename):
    """Initializes instance of DatasetMetadata."""
    self._true_labels = {}
    self._target_classes = {}
    with open(filename) as f:
      reader = csv.reader(f)
      header_row = next(reader)
      try:
        row_idx_image_id = header_row.index('ImageId')
        row_idx_true_label = header_row.index('TrueLabel')
        row_idx_target_class = header_row.index('TargetClass')
      except ValueError:
        raise IOError('Invalid format of dataset metadata.')
      for row in reader:
        if len(row) < len(header_row):
          # skip partial or empty lines
          continue
        try:
          image_id = row[row_idx_image_id]
          self._true_labels[image_id] = int(row[row_idx_true_label])
          self._target_classes[image_id] = int(row[row_idx_target_class])
        except (IndexError, ValueError):
          raise IOError('Invalid format of dataset metadata') 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def load_images(input_dir, metadata_file_path, batch_shape):
    """Retrieve numpy arrays of images and labels, read from a directory."""
    num_images = batch_shape[0]
    with open(metadata_file_path) as input_file:
        reader = csv.reader(input_file)
        header_row = next(reader)
        rows = list(reader)

    row_idx_image_id = header_row.index('ImageId')
    row_idx_true_label = header_row.index('TrueLabel')
    images = np.zeros(batch_shape)
    labels = np.zeros(num_images, dtype=np.int32)
    for idx in xrange(num_images):
        row = rows[idx]
        filepath = os.path.join(input_dir, row[row_idx_image_id] + '.png')

        with tf.gfile.Open(filepath, 'rb') as f:
            image = np.array(
                Image.open(f).convert('RGB')).astype(np.float) / 255.0
        images[idx, :, :, :] = image
        labels[idx] = int(row[row_idx_true_label])
    return images, labels 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def __getitem__(self, index):
        """This function returns a tuple that is further passed to collate_fn
        """
        vocab = self.vocab
        root = self.root
        ann_id = self.ids[index]
        img_id = ann_id[0]
        caption = self.dataset[img_id]['sentences'][ann_id[1]]['raw']
        path = self.dataset[img_id]['filename']

        image = Image.open(os.path.join(root, path)).convert('RGB')
        if self.transform is not None:
            image = self.transform(image)

        # Convert caption (string) to word ids.
        tokens = nltk.tokenize.word_tokenize(
            str(caption).lower())
        caption = []
        caption.append(vocab('<start>'))
        caption.extend([vocab(token) for token in tokens])
        caption.append(vocab('<end>'))
        target = torch.Tensor(caption)
        return image, target, index, img_id 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def __init__(self, data_path, data_split, vocab, cap_suffix='caps'):
        self.vocab = vocab
        loc = data_path + '/'

        # Captions
        self.captions = []
        with open(loc+'%s_%s.txt' % (data_split, cap_suffix), 'rb') as f:
            for line in f:
                tmp = line.strip()
                if type(tmp) == bytes:
                    tmp = bytes.decode(tmp)
                self.captions.append(tmp)

        # Image features
        self.images = np.load(loc+'%s_ims.npy' % data_split)
        self.length = len(self.captions)
        # rkiros data has redundancy in images, we divide by 5, 10crop doesn't
        if self.images.shape[0] != self.length:
            self.im_div = 5
        else:
            self.im_div = 1
        # the development set for coco is large and so validation would be slow
        if data_split == 'dev':
            self.length = 5000 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def get_data(img_path):
    """get the (1, 3, h, w) np.array data for the supplied image
                Args:
                    img_path (string): the input image path

                Returns:
                    np.array: image data in a (1, 3, h, w) shape

    """
    mean = np.array([123.68, 116.779, 103.939])  # (R,G,B)
    img = Image.open(img_path)
    img = np.array(img, dtype=np.float32)
    reshaped_mean = mean.reshape(1, 1, 3)
    img = img - reshaped_mean
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2)
    img = np.expand_dims(img, axis=0)
    return img 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def __init__(self, root_dir, flist_name,
                 rgb_mean = (117, 117, 117),
                 cut_off_size = None,
                 data_name = "data",
                 label_name = "softmax_label"):
        super(FileIter, self).__init__()
        self.root_dir = root_dir
        self.flist_name = os.path.join(self.root_dir, flist_name)
        self.mean = np.array(rgb_mean)  # (R, G, B)
        self.cut_off_size = cut_off_size
        self.data_name = data_name
        self.label_name = label_name

        self.num_data = len(open(self.flist_name, 'r').readlines())
        self.f = open(self.flist_name, 'r')
        self.data, self.label = self._read()
        self.cursor = -1 

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import open [as 别名]
def get_caltech101_data():
    url = "https://s3.us-east-2.amazonaws.com/mxnet-public/101_ObjectCategories.tar.gz"
    dataset_name = "101_ObjectCategories"
    data_folder = "data"
    if not os.path.isdir(data_folder):
        os.makedirs(data_folder)
    tar_path = mx.gluon.utils.download(url, path=data_folder)
    if (not os.path.isdir(os.path.join(data_folder, "101_ObjectCategories")) or
        not os.path.isdir(os.path.join(data_folder, "101_ObjectCategories_test"))):
        tar = tarfile.open(tar_path, "r:gz")
        tar.extractall(data_folder)
        tar.close()
        print('Data extracted')
    training_path = os.path.join(data_folder, dataset_name)
    testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
    return training_path, testing_path