upsampler/utils/inference_utils.py

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import random
import math
import argparse
import torch
from torch.utils import data
from torch.nn import functional as F
from torch import autograd
from torch.nn import init
import torchvision.transforms as transforms
from scripts.align_all_parallel import get_landmark
    
def visualize(img_arr, dpi):
    plt.figure(figsize=(10,10),dpi=dpi)
    plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))
    plt.axis('off')
    plt.show()

def save_image(img, filename):
    tmp = ((img.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
    cv2.imwrite(filename, cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR))
    
def load_image(filename):
    transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
    ])
    
    img = Image.open(filename)
    img = transform(img)
    return img.unsqueeze(dim=0)   

def get_video_crop_parameter(filepath, predictor, padding=[256,256,256,256]):
    if type(filepath) == str:
        img = dlib.load_rgb_image(filepath)
    else:
        img = filepath
    lm = get_landmark(img, predictor)
    if lm is None:
        return None
    lm_chin          = lm[0  : 17]  # left-right
    lm_eyebrow_left  = lm[17 : 22]  # left-right
    lm_eyebrow_right = lm[22 : 27]  # left-right
    lm_nose          = lm[27 : 31]  # top-down
    lm_nostrils      = lm[31 : 36]  # top-down
    lm_eye_left      = lm[36 : 42]  # left-clockwise
    lm_eye_right     = lm[42 : 48]  # left-clockwise
    lm_mouth_outer   = lm[48 : 60]  # left-clockwise
    lm_mouth_inner   = lm[60 : 68]  # left-clockwise
    
    scale = 64. / (np.mean(lm_eye_right[:,0])-np.mean(lm_eye_left[:,0]))
    center = ((np.mean(lm_eye_right, axis=0)+np.mean(lm_eye_left, axis=0)) / 2) * scale
    h, w = round(img.shape[0] * scale), round(img.shape[1] * scale)
    left = max(round(center[0] - padding[0]), 0) // 8 * 8
    right = min(round(center[0] + padding[1]), w) // 8 * 8
    top = max(round(center[1] - padding[2]), 0) // 8 * 8
    bottom = min(round(center[1] + padding[3]), h) // 8 * 8
    return h,w,top,bottom,left,right,scale

def tensor2cv2(img):
    tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
    return cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR)

def noise_regularize(noises):
    loss = 0

    for noise in noises:
        size = noise.shape[2]

        while True:
            loss = (
                loss
                + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2)
                + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2)
            )

            if size <= 8:
                break

            #noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2])
            #noise = noise.mean([3, 5])
            noise = F.interpolate(noise, scale_factor=0.5, mode='bilinear')
            size //= 2

    return loss


def noise_normalize_(noises):
    for noise in noises:
        mean = noise.mean()
        std = noise.std()

        noise.data.add_(-mean).div_(std)


def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
    lr_ramp = min(1, (1 - t) / rampdown)
    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
    lr_ramp = lr_ramp * min(1, t / rampup)

    return initial_lr * lr_ramp


def latent_noise(latent, strength):
    noise = torch.randn_like(latent) * strength

    return latent + noise


def make_image(tensor):
    return (
        tensor.detach()
        .clamp_(min=-1, max=1)
        .add(1)
        .div_(2)
        .mul(255)
        .type(torch.uint8)
        .permute(0, 2, 3, 1)
        .to("cpu")
        .numpy()
    )


# from pix2pixeHD
# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8):
    if n_label == 0:
        return tensor2im(label_tensor, imtype)
    label_tensor = label_tensor.cpu().float()    
    if label_tensor.size()[0] > 1:
        label_tensor = label_tensor.max(0, keepdim=True)[1]
    label_tensor = Colorize(n_label)(label_tensor)
    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
    return label_numpy.astype(imtype)

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: # cityscape
        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)
    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] = r
            cmap[i, 1] = g
            cmap[i, 2] = 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