`handbag_data/github_datasets/iGAN/lib/utils.py`

144 lines
from __future__ import print_function

import inspect
import re
import numpy as np
import cv2
import os
import collections
try:
    import pickle as pickle
except ImportError:
    import pickle


def debug_trace():
    from PyQt4.QtCore import pyqtRemoveInputHook
    from pdb import set_trace
    pyqtRemoveInputHook()
    set_trace()


def info(object, spacing=10, collapse=1):
    """Print methods and doc strings.
    Takes module, class, list, dictionary, or string."""
    methodList = [e for e in dir(object) if isinstance(getattr(object, e), collections.Callable)]
    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
    print("\n".join(["%s %s" %
                     (method.ljust(spacing),
                      processFunc(str(getattr(object, method).__doc__)))
                     for method in methodList]))


def PickleLoad(file_name):
    try:
        with open(file_name, 'rb') as f:
            data = pickle.load(f)
    except UnicodeDecodeError:
        with open(file_name, 'rb') as f:
            data = pickle.load(f, encoding='latin1')
    return data


def PickleSave(file_name, data):
    with open(file_name, "wb") as f:
        pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)


def varname(p):
    for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
        m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
        if m:
            return m.group(1)


def interp_z(z0, z1, ratio, interp='linear'):
    if interp == 'linear':
        z_t = (1 - ratio) * z0 + ratio * z1

    if interp == 'slerp':
        N = len(z0)
        z_t = []
        for i in range(N):
            z0_i = z0[i]
            z1_i = z1[i]
            z0_n = z0_i / np.linalg.norm(z0_i)
            z1_n = z1_i / np.linalg.norm(z1_i)
            omega = np.arccos(np.dot(z0_n, z1_n))
            sin_omega = np.sin(omega)
            if sin_omega == 0:
                z_i = interp_z(z0_i, z1_i, ratio, 'linear')
            else:
                z_i = np.sin((1 - ratio) * omega) / sin_omega * z0_i + np.sin(ratio * omega) / sin_omega * z1_i
            z_t.append(z_i[np.newaxis, ...])
        z_t = np.concatenate(z_t, axis=0)
    return z_t


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 CVShow(im, im_name='', wait=1):
    if len(im.shape) >= 3 and im.shape[2] == 3:
        im_show = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    else:
        im_show = im

    cv2.imshow(im_name, im_show)
    cv2.waitKey(wait)
    return im_show


def average_image(imgs, weights):
    im_weights = np.tile(weights[:, np.newaxis, np.newaxis, np.newaxis], (1, imgs.shape[1], imgs.shape[2], imgs.shape[3]))
    imgs_f = imgs.astype(np.float32)
    weights_norm = np.mean(im_weights)
    average_f = np.mean(imgs_f * im_weights, axis=0) / weights_norm
    average = average_f.astype(np.uint8)
    return average


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 grid_vis(X, nh, nw):  # [buggy]
    if X.shape[0] == 1:
        return X[0]

    # nc = 3
    if X.ndim == 3:
        X = X[..., np.newaxis]
    if X.shape[-1] == 1:
        X = np.tile(X, [1, 1, 1, 3])

    h, w = X[0].shape[:2]

    if X.dtype == np.uint8:
        img = np.ones((h * nh, w * nw, 3), np.uint8) * 255
    else:
        img = np.ones((h * nh, w * nw, 3), X.dtype)

    for n, x in enumerate(X):
        j = n // nw
        i = n % nw
        img[j * h:j * h + h, i * w:i * w + w, :] = x
    img = np.squeeze(img)
    return img