The output is whole dark image

[SikkeyHuang]

I cannot get correct result from the python code, but when I use the command like ' autocolorize grayscale.png -o colorized.png' I get the correct results

My code is following:

import cv2 import os import autocolorize

path2 = 'input/' path3 = 'output/' files= os.listdir(path2)

classifier = autocolorize.load_default_classifier()

for ff in files: gray_image = cv2.imread(path2+ff) rgb_image = autocolorize.colorize(gray_image, classifier=classifier) cv2.imwrite(path3+rgb_image,rgb_image)

[hermosayhl]

I cannot get correct result from the python code, but when I use the command like ' autocolorize grayscale.png -o colorized.png' I get the correct results

My code is following:

import cv2 import os import autocolorize

path2 = 'input/' path3 = 'output/' files= os.listdir(path2)

classifier = autocolorize.load_default_classifier()

for ff in files: gray_image = cv2.imread(path2+ff) rgb_image = autocolorize.colorize(gray_image, classifier=classifier) cv2.imwrite(path3+rgb_image,rgb_image)

How do you make out with it ?

[guangpintao]

You need to use the data reading and writing method in the code:

import cv2
import os
import autocolorize
import numpy as np
import torch
import time
from tqdm import tqdm

def load(path, dtype=np.float64):
    """
    Loads an image from file.
    Parameters
    ----------
    path : str
        Path to image file.
    dtype : np.dtype
        Defaults to ``np.float64``, which means the image will be returned as a
        float with values between 0 and 1. If ``np.uint8`` is specified, the
        values will be between 0 and 255 and no conversion cost will be
        incurred.
    """
    import skimage.io
    im = skimage.io.imread(path)
    if dtype == np.uint8:
        return im
    elif dtype in {np.float16, np.float32, np.float64}:
        return im.astype(dtype) / 255
    else:
        raise ValueError('Unsupported dtype')

def save(path, im):
    """
    Saves an image to file.
    If the image is type float, it will assume to have values in [0, 1].
    Parameters
    ----------
    path : str
        Path to which the image will be saved.
    im : ndarray (image)
        Image.
    """
    from PIL import Image
    if im.dtype == np.uint8:
        pil_im = Image.fromarray(im)
    else:
        pil_im = Image.fromarray((im * 255).astype(np.uint8))
    pil_im.save(path)

path2 = '/home/rpf/tgp/Colorization/colorization_valset/celebahq_val_256/'
path3 = '/home/rpf/tgp/Colorization/colorization_valset/celebahq_val_256_results/'
files = os.listdir(path2)
import caffe

caffe.set_mode_gpu()
classifier = autocolorize.load_default_classifier()
time_costs = []
skip = 5
for ff in tqdm(files):
    skip -= 1
    gray_image = load(path2 + ff)
    # torch.cuda.synchronize()
    start = time.time()
    rgb_image = autocolorize.colorize(gray_image, classifier=classifier)
    # torch.cuda.synchronize()
    end = time.time()
    cost = (end - start) * 1000
    if skip < 0:
        time_costs.append(cost)
    save(path3 + ff, rgb_image)
print(np.average(time_costs))