Deep3DFaceRecon_pytorch/util/util.py

"""This script contains basic utilities for Deep3DFaceRecon_pytorch
"""
from __future__ import print_function

import argparse
import importlib
import os
from argparse import Namespace

import numpy as np
import torch
import torchvision
from PIL import Image


def str2bool(v):
    if isinstance(v, bool):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("Boolean value expected.")


def copyconf(default_opt, **kwargs):
    conf = Namespace(**vars(default_opt))
    for key in kwargs:
        setattr(conf, key, kwargs[key])
    return conf


def genvalconf(train_opt, **kwargs):
    conf = Namespace(**vars(train_opt))
    attr_dict = train_opt.__dict__
    for key, value in attr_dict.items():
        if "val" in key and key.split("_")[0] in attr_dict:
            setattr(conf, key.split("_")[0], value)

    for key in kwargs:
        setattr(conf, key, kwargs[key])

    return conf


def find_class_in_module(target_cls_name, module):
    target_cls_name = target_cls_name.replace("_", "").lower()
    clslib = importlib.import_module(module)
    cls = None
    for name, clsobj in clslib.__dict__.items():
        if name.lower() == target_cls_name:
            cls = clsobj

    assert (
        cls is not None
    ), "In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (
        module,
        target_cls_name,
    )

    return cls


def tensor2im(input_image, imtype=np.uint8):
    """ "Converts a Tensor array into a numpy image array.

    Parameters:
        input_image (tensor) --  the input image tensor array, range(0, 1)
        imtype (type)        --  the desired type of the converted numpy array
    """
    if not isinstance(input_image, np.ndarray):
        if isinstance(input_image, torch.Tensor):  # get the data from a variable
            image_tensor = input_image.data
        else:
            return input_image
        image_numpy = image_tensor.clamp(0.0, 1.0).cpu().float().numpy()  # convert it into a numpy array
        if image_numpy.shape[0] == 1:  # grayscale to RGB
            image_numpy = np.tile(image_numpy, (3, 1, 1))
        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0  # post-processing: tranpose and scaling
    else:  # if it is a numpy array, do nothing
        image_numpy = input_image
    return image_numpy.astype(imtype)


def diagnose_network(net, name="network"):
    """Calculate and print the mean of average absolute(gradients)

    Parameters:
        net (torch network) -- Torch network
        name (str) -- the name of the network
    """
    mean = 0.0
    count = 0
    for param in net.parameters():
        if param.grad is not None:
            mean += torch.mean(torch.abs(param.grad.data))
            count += 1
    if count > 0:
        mean = mean / count
    print(name)
    print(mean)


def save_image(image_numpy, image_path, aspect_ratio=1.0):
    """Save a numpy image to the disk

    Parameters:
        image_numpy (numpy array) -- input numpy array
        image_path (str)          -- the path of the image
    """

    image_pil = Image.fromarray(image_numpy)
    h, w, _ = image_numpy.shape

    if aspect_ratio is None:
        pass
    elif aspect_ratio > 1.0:
        image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.Resampling.BICUBIC)
    elif aspect_ratio < 1.0:
        image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.Resampling.BICUBIC)
    image_pil.save(image_path)


def print_numpy(x, val=True, shp=False):
    """Print the mean, min, max, median, std, and size of a numpy array

    Parameters:
        val (bool) -- if print the values of the numpy array
        shp (bool) -- if print the shape of the numpy array
    """
    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):
    """create empty directories if they don't exist

    Parameters:
        paths (str list) -- a list of directory paths
    """
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    """create a single empty directory if it didn't exist

    Parameters:
        path (str) -- a single directory path
    """
    if not os.path.exists(path):
        os.makedirs(path)


def correct_resize_label(t, size):
    device = t.device
    t = t.detach().cpu()
    resized = []
    for i in range(t.size(0)):
        one_t = t[i, :1]
        one_np = np.transpose(one_t.numpy().astype(np.uint8), (1, 2, 0))
        one_np = one_np[:, :, 0]
        one_image = Image.fromarray(one_np).resize(size, Image.NEAREST)
        resized_t = torch.from_numpy(np.array(one_image)).long()
        resized.append(resized_t)
    return torch.stack(resized, dim=0).to(device)


def correct_resize(t, size, mode=Image.Resampling.BICUBIC):
    device = t.device
    t = t.detach().cpu()
    resized = []
    for i in range(t.size(0)):
        one_t = t[i : i + 1]
        one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.Resampling.BICUBIC)
        resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
        resized.append(resized_t)
    return torch.stack(resized, dim=0).to(device)


def draw_landmarks(img, landmark, color="r", step=2):
    """
    Return:
        img              -- numpy.array, (B, H, W, 3) img with landmark, RGB order, range (0, 255)


    Parameters:
        img              -- numpy.array, (B, H, W, 3), RGB order, range (0, 255)
        landmark         -- numpy.array, (B, 68, 2), y direction is opposite to v direction
        color            -- str, 'r' or 'b' (red or blue)
    """
    if color == "r":
        c = np.array([255.0, 0, 0])
    else:
        c = np.array([0, 0, 255.0])

    _, H, W, _ = img.shape
    img, landmark = img.copy(), landmark.copy()
    landmark[..., 1] = H - 1 - landmark[..., 1]
    landmark = np.round(landmark).astype(np.int32)
    for i in range(landmark.shape[1]):
        x, y = landmark[:, i, 0], landmark[:, i, 1]
        for j in range(-step, step):
            for k in range(-step, step):
                u = np.clip(x + j, 0, W - 1)
                v = np.clip(y + k, 0, H - 1)
                for m in range(landmark.shape[0]):
                    img[m, v[m], u[m]] = c
    return img