vid2vid/util/util.py

from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import inspect, re
import numpy as np
import os
import collections
from PIL import Image
import cv2

# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    if isinstance(image_tensor, list):
        image_numpy = []
        for i in range(len(image_tensor)):
            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
        return image_numpy

    if isinstance(image_tensor, torch.autograd.Variable):
        image_tensor = image_tensor.data
    if len(image_tensor.size()) == 4:
        image_tensor = image_tensor[0]
    image_numpy = image_tensor.cpu().float().numpy()
    if normalize:
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    else:
        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
    #image_numpy = (np.transpose(image_numpy, (1, 2, 0)) * std + mean)  * 255.0        
    image_numpy = np.clip(image_numpy, 0, 255)
    if image_numpy.shape[2] == 1:        
        image_numpy = image_numpy[:,:,0]
    return image_numpy.astype(imtype)

def tensor2label(output, n_label, imtype=np.uint8):
    if isinstance(output, torch.autograd.Variable):
        output = output.data
    if len(output.size()) == 4:
        output = output[0]
    output = output.cpu().float()    
    if output.size()[0] > 1:
        output = output.max(0, keepdim=True)[1]
    #print(output.size())
    output = Colorize(n_label)(output)
    output = np.transpose(output.numpy(), (1, 2, 0))
    #img = Image.fromarray(output, "RGB")
    return output.astype(imtype)

def tensor2flow(output, imtype=np.uint8):
    if isinstance(output, torch.autograd.Variable):
        output = output.data
    if len(output.size()) == 4:
        output = output[0]
    output = output.cpu().float().numpy()
    output = np.transpose(output, (1, 2, 0))
    #mag = np.max(np.sqrt(output[:,:,0]**2 + output[:,:,1]**2)) 
    #print(mag)
    hsv = np.zeros((output.shape[0], output.shape[1], 3), dtype=np.uint8)
    hsv[:, :, 0] = 255
    hsv[:, :, 1] = 255
    mag, ang = cv2.cartToPolar(output[..., 0], output[..., 1])
    hsv[..., 0] = ang * 180 / np.pi / 2
    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
    rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
    return rgb

def save_image(image_numpy, image_path):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)

def print_numpy(x, val=True, shp=False):
    x = x.astype(np.float64)
    if shp:
        print('shape,', x.shape)
    if val:
        x = x.flatten()
        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))

def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)

def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)

def uint82bin(n, count=8):
    """returns the binary of integer n, count refers to amount of bits"""
    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])

def labelcolormap(N):
    if N == 35: # Cityscapes train
        cmap = np.array([(  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (111, 74,  0), ( 81,  0, 81),
                     (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
                     (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220,  0),
                     (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), (  0,  0, 70),
                     (  0, 60,100), (  0,  0, 90), (  0,  0,110), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,142)], 
                     dtype=np.uint8)
    elif N == 20: # Cityscapes eval
        cmap = np.array([(128, 64,128), (244, 35,232), ( 70, 70, 70), (102,102,156), (190,153,153), (153,153,153), (250,170, 30), 
                         (220,220,  0), (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), 
                         (  0,  0, 70), (  0, 60,100), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,  0)], 
                         dtype=np.uint8)
    else:
        cmap = np.zeros((N, 3), dtype=np.uint8)
        for i in range(N):
            r, g, b = 0, 0, 0            
            id = i
            for j in range(7):
                str_id = uint82bin(id)
                r = r ^ (np.uint8(str_id[-1]) << (7-j))
                g = g ^ (np.uint8(str_id[-2]) << (7-j))
                b = b ^ (np.uint8(str_id[-3]) << (7-j))
                id = id >> 3
            cmap[i, 0], cmap[i, 1], cmap[i, 2] = r, g, b             
    return cmap

def colormap(n):
    cmap = np.zeros([n, 3]).astype(np.uint8)
    for i in np.arange(n):
        r, g, b = np.zeros(3)

        for j in np.arange(8):
            r = r + (1 << (7-j))*((i & (1 << (3*j))) >> (3*j))
            g = g + (1 << (7-j))*((i & (1 << (3*j+1))) >> (3*j+1))
            b = b + (1 << (7-j))*((i & (1 << (3*j+2))) >> (3*j+2))

        cmap[i, :] = np.array([r, g, b])

    return cmap

class Colorize(object):
    def __init__(self, n=35):
        self.cmap = labelcolormap(n)
        self.cmap = torch.from_numpy(self.cmap[:n])

    def __call__(self, gray_image):
        size = gray_image.size()
        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)

        for label in range(0, len(self.cmap)):
            mask = (label == gray_image[0]).cpu()
            color_image[0][mask] = self.cmap[label][0]
            color_image[1][mask] = self.cmap[label][1]
            color_image[2][mask] = self.cmap[label][2]

        return color_image