def main(): im = mk_big_char_im() display.display(im) mk_rand_bin_img_from_mask(im) return im
def mk_rand_bin_img_from_mask(mask_im): bin_font = ImageFont.truetype('consola.ttf', 9) bin_color = (0xc8, 0xff, 0xc8, 0xff) im = Image.new('RGBA', mask_im.size) draw = ImageDraw.Draw(im) width, height = im.size one = bin_font.getsize('1') zero = bin_font.getsize('0') start_x = 0 start_y = 0 import random for start_y in xrange(start_y, height, zero[1]): randstr = ''.join(random.choice(['1', '0']) for _ in xrange(100)) draw.text((start_x, start_y), randstr, font=bin_font, fill=bin_color) display.display(im) import ImageMath bands = im.split() bands = [ ImageMath.eval( 'convert(min(mask, band), "L")', mask = mask_im, band = band ) for band in bands ] new_im = Image.merge('RGBA', bands) with file('foo.jpg', 'wb') as f: new_im.save(f, format='jpeg') display.display(new_im)
untar=True) data_dir = pathlib.Path(data_dir) #Numero de imagenes image_count = len(list(data_dir.glob('*/*.jpg'))) image_count #Directorios CLASS_NAMES = np.array( [item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"]) CLASS_NAMES #Ver algunos ejemplos roses = list(data_dir.glob('roses/*')) for image_path in roses[:3]: display.display(Image.open(str(image_path))) #Cargar imagenes using KERAS.PREPROCESSING image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) BATCH_SIZE = 32 IMG_HEIGHT = 224 IMG_WIDTH = 224 STEPS_PER_EPOCH = np.ceil(image_count / BATCH_SIZE) train_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=BATCH_SIZE, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH),