How to use base_image method in avocado

Best Python code snippet using avocado_python

assemble_data.py

Source:assemble_data.py

1import glob2import numpy as np3import matplotlib.pyplot as plt4from scipy import misc5import os6from tqdm import tqdm7atri_dir = 'attribute_resized/'8mask_dir = 'segmentation_resized/'9output_dir = 'semantic_map/'10file_name_arr = [] # [ISIC_00000, ISIC_000001, ISIC_000003, ...]11for file in glob.glob(atri_dir+'*.png'):12	temp = file.split('/')[-1].split('_')13	file_name = temp[0]+'_'+temp[1]14	if file_name not in file_name_arr:15	    file_name_arr.append(file_name)16if not os.path.exists(output_dir):17    os.makedirs(output_dir)18for family in tqdm(file_name_arr):19	# Create a zero filled base image20	for i, file in enumerate(glob.glob(atri_dir+family+'*.png')):21		# Read the image22		read_image = misc.imread(file, flatten=True)23		border_color = read_image[0,0]24		read_image[read_image == border_color] = 025		read_image[read_image > 0] = 25526		read_image = np.int8(read_image/255)27		if i == 0:28			mask = misc.imread(mask_dir+family+'_segmentation.png', flatten=True)29			base_image = np.ones(read_image.shape, dtype=int) # Healthy Skin is 130			border_mask_color = mask[0,0]31			base_image[mask == border_mask_color] = 032			mask[mask == border_mask_color] = 033			mask[mask > 0] = 25534			mask = np.int8(mask/255)35			base_image += mask # Common Lesion is 236		type_file = file.split('/')[-1].split('_')[3]37		if type_file == 'pigment': # 338			base_image += read_image39			if base_image[base_image > 3].any():40				base_image[base_image > 3] = 341		elif type_file == 'negative': # 442			base_image += read_image*243			if base_image[base_image > 4].any():44				base_image[base_image > 4] = 445		elif type_file.startswith('streaks'): # 546			base_image += read_image*347			if base_image[base_image > 5].any():48				base_image[base_image > 5] = 549		elif type_file == 'milia': # 650			base_image += read_image*451			if base_image[base_image > 6].any():52				base_image[base_image > 6] = 653		elif type_file.startswith('globules'): #754			base_image += read_image*555			if base_image[base_image > 7].any():56				base_image[base_image > 7] = 757		else:58			print('ERROR: Invalid File Found!!!!')...

combinators.py

Source:combinators.py

1# nuScenes dev-kit.2# Code written by Freddy Boulton, 2020.3from functools import reduce4from typing import List5import cv26import numpy as np7from nuscenes.prediction.input_representation.interface import Combinator8def add_foreground_to_image(base_image: np.ndarray,9                            foreground_image: np.ndarray) -> np.ndarray:10    """11    Overlays a foreground image on top of a base image without mixing colors. Type uint8.12    :param base_image: Image that will be the background. Type uint8.13    :param foreground_image: Image that will be the foreground.14    :return: Image Numpy array of type uint8.15    """16    if not base_image.shape == foreground_image.shape:17        raise ValueError("base_image and foreground image must have the same shape."18                         " Received {} and {}".format(base_image.shape, foreground_image.shape))19    if not (base_image.dtype == "uint8" and foreground_image.dtype == "uint8"):20        raise ValueError("base_image and foreground image must be of type 'uint8'."21                         " Received {} and {}".format(base_image.dtype, foreground_image.dtype))22    img2gray = cv2.cvtColor(foreground_image, cv2.COLOR_BGR2GRAY)23    _, mask = cv2.threshold(img2gray, 0, 255, cv2.THRESH_BINARY)24    mask_inv = cv2.bitwise_not(mask)25    img1_bg = cv2.bitwise_and(base_image, base_image, mask=mask_inv)26    img2_fg = cv2.bitwise_and(foreground_image, foreground_image, mask=mask)27    combined_image = cv2.add(img1_bg, img2_fg)28    return combined_image29class Rasterizer(Combinator):30    """31    Combines images into a three channel image.32    """33    def combine(self, data: List[np.ndarray]) -> np.ndarray:34        """35        Combine three channel images into a single image.36        :param data: List of images to combine.37        :return: Numpy array representing image (type 'uint8')38        """39        # All images in the dict are the same shape40        image_shape = data[0].shape41        base_image = np.zeros(image_shape).astype("uint8")...

augmentation.py

Source:augmentation.py

1from typing import Optional2import albumentations as A3import numpy as np4def create_green_channel(base_image: np.ndarray, noise_scale: float = 25.0, max_shift: int = 20) -> np.ndarray:5    """Data augmentation6    Args:7        base_image: (height, width)8        noise_scale (int): original image range is about 0~2559        max_shift (int):10    Returns:11        green_image: (height, width)12    """13    green_image = np.zeros_like(base_image)14    # 1. Adding gaussian noise15    base_image = base_image + np.random.randn(*base_image.shape) * noise_scale16    # base_image = np.clip(base_image, 0.0, 255.0)  # is it necessary??, maybe not.17    height, width = base_image.shape18    # 2. shift randomly, up to 2 pixel19    x_shift = np.random.randint(-max_shift, max_shift + 1)20    x_min = max(0, x_shift)21    x_max = min(width, width + x_shift)22    x_min2 = max(0, -x_shift)23    x_max2 = min(width, width - x_shift)24    y_shift = np.random.randint(-max_shift, max_shift + 1)25    y_min = max(0, y_shift)26    y_max = min(height, height + y_shift)27    y_min2 = max(0, -y_shift)28    y_max2 = min(height, height - y_shift)29    green_image[x_min:x_max, y_min:y_max] = base_image[x_min2:x_max2, y_min2:y_max2]30    return green_image31def create_green_channel_albumentations(base_image: np.ndarray, transform: Optional[A.Compose] = None) -> np.ndarray:32    """Data augmentation33    Args:34        base_image: (height, width)35        transform (None or A.Compose): albumentations transform36    Returns:37        green_image: (height, width)38    """39    if transform is None:40        # For backward compatibility41        return create_green_channel(base_image)...

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