Edge-Connect: utils.py

import os
import sys
import time
import random
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

# 디렉토리를 받아 폴더를 만들어주는 함수
def create_dir(dir):
    if not os.path.exists(dir):
        os.makedirs(dir)

# 
def create_mask(width, height, mask_width, mask_height, x=None, y=None):
    mask = np.zeros((height, width)) # ex) 512x512가 0으로 채워진 이미지
    mask_x = x if x is not None else random.randint(0, width - mask_width)
    mask_y = y if y is not None else random.randint(0, height - mask_height)
    mask[mask_y:mask_y + mask_height, mask_x:mask_x + mask_width] = 1 # (x, y)로부터 mask_width, mask_height만큼 사각형 마스킹.
    return mask


def stitch_images(inputs, *outputs, img_per_row=2):
    # inputs: 입력 이미지 배열
    # *outputs: 출력 이미지 배열
    # img_per_row: 한 행에 표시할 이미지 수
    gap = 5 # 이미지 간격
    columns = len(outputs) + 1 # 입력 이미지를 그리기 위해 1을 추가

    width, height = inputs[0][:, :, 0].shape # 
    # 병합할 전체 이미지의 크기를 계산하고 새로운 이미지를 생성합니다.
    img = Image.new('RGB', (width * img_per_row * columns + gap * (img_per_row - 1), height * int(len(inputs) / img_per_row)))
    images = [inputs, *outputs] # 입력 이미지와 출력 이미지를 하나의 리스트로 결합

    for ix in range(len(inputs)):
        # 현재 이미지가 위치할 x 좌표를 계산
        xoffset = int(ix % img_per_row) * width * columns + int(ix % img_per_row) * gap
        # 현재 이미지가 위치할 y 좌표를 계산
        yoffset = int(ix / img_per_row) * height
        # 입력 및 출력 이미지 카테고리에 대해 반복
        for cat in range(len(images)):
            im = np.array((images[cat][ix]).cpu()).astype(np.uint8).squeeze()
            im = Image.fromarray(im)
            img.paste(im, (xoffset + cat * width, yoffset))

    return img

# 이미지를 입력받아 보여줌
def imshow(img, title=''):
    fig = plt.gcf()
    fig.canvas.set_window_title(title)
    plt.axis('off')
    plt.imshow(img, interpolation='none')
    plt.show()

# 이미지를 해당 경로에 세이브
def imsave(img, path):
    im = Image.fromarray(img.cpu().numpy().astype(np.uint8).squeeze())
    im.save(path)


class Progbar(object):
    """Displays a progress bar.

    Arguments:
        target: Total number of steps expected, None if unknown.
        width: Progress bar width on screen.
        verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
        stateful_metrics: Iterable of string names of metrics that
            should *not* be averaged over time. Metrics in this list
            will be displayed as-is. All others will be averaged
            by the progbar before display.
        interval: Minimum visual progress update interval (in seconds).
    """

    def __init__(self, target, width=25, verbose=1, interval=0.05,
                 stateful_metrics=None):
        # target: 목표 단계 수
        # width: 진행 표시줄 너비
        # verbose: 출력 모드
        # interval: 업데이트 간격
        # stateful_metrics: 상태 유지 메트릭
        self.target = target
        self.width = width
        self.verbose = verbose
        self.interval = interval
        if stateful_metrics:
            self.stateful_metrics = set(stateful_metrics)
        else:
            self.stateful_metrics = set()

        self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
                                  sys.stdout.isatty()) or
                                 'ipykernel' in sys.modules or
                                 'posix' in sys.modules)
        self._total_width = 0
        self._seen_so_far = 0
        # We use a dict + list to avoid garbage collection
        # issues found in OrderedDict
        self._values = {}
        self._values_order = []
        self._start = time.time()
        self._last_update = 0

    def update(self, current, values=None):
        """Updates the progress bar.

        Arguments:
            current: Index of current step.
            values: List of tuples:
                `(name, value_for_last_step)`.
                If `name` is in `stateful_metrics`,
                `value_for_last_step` will be displayed as-is.
                Else, an average of the metric over time will be displayed.
        """
        values = values or []
        for k, v in values:
            if k not in self._values_order:
                self._values_order.append(k)
            if k not in self.stateful_metrics:
                if k not in self._values:
                    self._values[k] = [v * (current - self._seen_so_far),
                                       current - self._seen_so_far]
                else:
                    self._values[k][0] += v * (current - self._seen_so_far)
                    self._values[k][1] += (current - self._seen_so_far)
            else:
                self._values[k] = v
        self._seen_so_far = current

        now = time.time()
        info = ' - %.0fs' % (now - self._start)
        if self.verbose == 1:
            if (now - self._last_update < self.interval and
                    self.target is not None and current < self.target):
                return

            prev_total_width = self._total_width
            if self._dynamic_display:
                sys.stdout.write('\b' * prev_total_width)
                sys.stdout.write('\r')
            else:
                sys.stdout.write('\n')

            if self.target is not None:
                numdigits = int(np.floor(np.log10(self.target))) + 1
                barstr = '%%%dd/%d [' % (numdigits, self.target)
                bar = barstr % current
                prog = float(current) / self.target
                prog_width = int(self.width * prog)
                if prog_width > 0:
                    bar += ('=' * (prog_width - 1))
                    if current < self.target:
                        bar += '>'
                    else:
                        bar += '='
                bar += ('.' * (self.width - prog_width))
                bar += ']'
            else:
                bar = '%7d/Unknown' % current

            self._total_width = len(bar)
            sys.stdout.write(bar)

            if current:
                time_per_unit = (now - self._start) / current
            else:
                time_per_unit = 0
            if self.target is not None and current < self.target:
                eta = time_per_unit * (self.target - current)
                if eta > 3600:
                    eta_format = '%d:%02d:%02d' % (eta // 3600,
                                                   (eta % 3600) // 60,
                                                   eta % 60)
                elif eta > 60:
                    eta_format = '%d:%02d' % (eta // 60, eta % 60)
                else:
                    eta_format = '%ds' % eta

                info = ' - ETA: %s' % eta_format
            else:
                if time_per_unit >= 1:
                    info += ' %.0fs/step' % time_per_unit
                elif time_per_unit >= 1e-3:
                    info += ' %.0fms/step' % (time_per_unit * 1e3)
                else:
                    info += ' %.0fus/step' % (time_per_unit * 1e6)

            for k in self._values_order:
                info += ' - %s:' % k
                if isinstance(self._values[k], list):
                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
                    if abs(avg) > 1e-3:
                        info += ' %.4f' % avg
                    else:
                        info += ' %.4e' % avg
                else:
                    info += ' %s' % self._values[k]

            self._total_width += len(info)
            if prev_total_width > self._total_width:
                info += (' ' * (prev_total_width - self._total_width))

            if self.target is not None and current >= self.target:
                info += '\n'

            sys.stdout.write(info)
            sys.stdout.flush()

        elif self.verbose == 2:
            if self.target is None or current >= self.target:
                for k in self._values_order:
                    info += ' - %s:' % k
                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
                    if avg > 1e-3:
                        info += ' %.4f' % avg
                    else:
                        info += ' %.4e' % avg
                info += '\n'

                sys.stdout.write(info)
                sys.stdout.flush()

        self._last_update = now

    def add(self, n, values=None):
        self.update(self._seen_so_far + n, values)