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import os
import re
import cv2
import yaml
import torch
import hashlib
import argparse
import albumentations as A
from albumentations.core.transforms_interface import ImageOnlyTransform
import numpy as np
from PIL import Image
from threading import Thread
from easydict import EasyDict
VID_EXTS = ('mp4', 'avi', 'h264', 'mkv', 'mov', 'flv', 'wmv', 'webm', 'ts', 'm4v', 'vob', '3gp', '3g2', 'rm', 'rmvb', 'ogv', 'ogg', 'drc', 'gif', 'gifv', 'mng', 'avi', 'mov', 'qt', 'wmv', 'yuv', 'rm', 'rmvb', 'asf', 'amv', 'mp4', 'm4p', 'm4v', 'mpg', 'mp2', 'mpeg', 'mpe', 'mpv', 'mpg', 'mpeg', 'm2v', 'm4v', 'svi', '3gp', '3g2', 'mxf', 'roq', 'nsv', 'flv', 'f4v', 'f4p', 'f4a', 'f4b')
IMG_EXTS = ('jpg', 'jpeg', 'bmp', 'png', 'ppm', 'pgm', 'pbm', 'pnm', 'webp', 'sr', 'ras', 'tiff', 'tif', 'exr', 'hdr', 'pic', 'dib', 'jpe', 'jp2', 'j2k', 'jpf', 'jpx', 'jpm', 'mj2', 'jxr', 'hdp', 'wdp', 'cur', 'ico', 'ani', 'icns', 'bpg', 'jp2', 'j2k', 'jpf', 'jpx', 'jpm', 'mj2', 'jxr', 'hdp', 'wdp', 'cur', 'ico', 'ani', 'icns', 'bpg')
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--source', '-s', type=str, help="Path to the source. Single image, video, directory of images, directory of videos is supported.")
parser.add_argument('--dest', '-d', type=str, default=None, help="Path to destination. Results will be stored in current directory if not specified.")
parser.add_argument('--type', '-t', type=str, default='rgba', help="Specify output type. If not specified, output results will make the background transparent. Please refer to the documentation for other types.")
parser.add_argument('--reverse', '-R', action='store_true', help="Output will be reverse and foreground will be removed instead of background if specified.")
parser.add_argument('--format', '-f', type=str, default=None, help="Specify output format. If not specified, it will be saved with the format of input.")
parser.add_argument('--resize', '-r', type=str, default='static', help="Specify resizing method. If not specified, static resize will be used. Choose from (static|dynamic).")
parser.add_argument('--jit', '-j', action='store_true', help="Speed up inference speed by using torchscript, but decreases output quality.")
parser.add_argument('--device', '-D', type=str, default=None, help="Designate device. If not specified, it will find available device.")
parser.add_argument('--mode', '-m', type=str, default='base', help="choose between base and fast mode. Also, use base-nightly for nightly release checkpoint.")
parser.add_argument('--ckpt', '-c', type=str, default=None, help="Designate checkpoint. If not specified, it will download or load pre-downloaded default checkpoint.")
parser.add_argument('--threshold', '-th', type=str, default=None, help="Designate threshold. If specified, it will output hard prediction above threshold. If not specified, it will output soft prediction.")
return parser.parse_args()
def get_backend():
if torch.cuda.is_available():
return "cuda:0"
elif torch.backends.mps.is_available():
return "mps:0"
else:
return "cpu"
def load_config(config_dir, easy=True):
cfg = yaml.load(open(config_dir), yaml.FullLoader)
if easy is True:
cfg = EasyDict(cfg)
return cfg
def get_format(source):
img_count = len([i for i in source if i.lower().endswith(IMG_EXTS)])
vid_count = len([i for i in source if i.lower().endswith(VID_EXTS)])
if img_count * vid_count != 0:
return ''
elif img_count != 0:
return 'Image'
elif vid_count != 0:
return 'Video'
else:
return ''
def sort(x):
convert = lambda text: int(text) if text.isdigit() else text.lower()
alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
return sorted(x, key=alphanum_key)
def download_and_unzip(filename, url, dest, unzip=True, **kwargs):
if not os.path.isdir(dest):
os.makedirs(dest, exist_ok=True)
if os.path.isfile(os.path.join(dest, filename)) is False:
os.system("wget -O {} {}".format(os.path.join(dest, filename), url))
elif 'md5' in kwargs.keys() and kwargs['md5'] != hashlib.md5(open(os.path.join(dest, filename), 'rb').read()).hexdigest():
os.system("wget -O {} {}".format(os.path.join(dest, filename), url))
if unzip:
os.system("unzip -o {} -d {}".format(os.path.join(dest, filename), dest))
os.system("rm {}".format(os.path.join(dest, filename)))
class dynamic_resize:
def __init__(self, L=1280):
self.L = L
def __call__(self, img):
size = list(img.size)
if (size[0] >= size[1]) and size[1] > self.L:
size[0] = size[0] / (size[1] / self.L)
size[1] = self.L
elif (size[1] > size[0]) and size[0] > self.L:
size[1] = size[1] / (size[0] / self.L)
size[0] = self.L
size = (int(round(size[0] / 32)) * 32, int(round(size[1] / 32)) * 32)
return img.resize(size, Image.BILINEAR)
class dynamic_resize_a(ImageOnlyTransform):
def __init__(self, L=1280, always_apply=False, p=1.0):
super(dynamic_resize_a, self).__init__(always_apply, p)
self.L = L
def apply(self, img, **params):
size = list(img.shape[:2])
if (size[0] >= size[1]) and size[1] > self.L:
size[0] = size[0] / (size[1] / self.L)
size[1] = self.L
elif (size[1] > size[0]) and size[0] > self.L:
size[1] = size[1] / (size[0] / self.L)
size[0] = self.L
size = (int(round(size[0] / 32)) * 32, int(round(size[1] / 32)) * 32)
return A.resize(img, height=size[0], width=size[1])
def get_transform_init_args_names(self):
return ("L",)
class static_resize:
def __init__(self, size=[1024, 1024]):
self.size = size
def __call__(self, img):
return img.resize(self.size, Image.BILINEAR)
class normalize:
def __init__(self, mean=None, std=None, div=255):
self.mean = mean if mean is not None else 0.0
self.std = std if std is not None else 1.0
self.div = div
def __call__(self, img):
img /= self.div
img -= self.mean
img /= self.std
return img
class tonumpy:
def __init__(self):
pass
def __call__(self, img):
img = np.array(img, dtype=np.float32)
return img
class totensor:
def __init__(self):
pass
def __call__(self, img):
img = img.transpose((2, 0, 1))
img = torch.from_numpy(img).float()
return img
class ImageLoader:
def __init__(self, root):
if os.path.isdir(root):
self.images = [os.path.join(root, f) for f in os.listdir(root) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]
self.images = sort(self.images)
elif os.path.isfile(root):
self.images = [root]
self.size = len(self.images)
def __iter__(self):
self.index = 0
return self
def __next__(self):
if self.index == self.size:
raise StopIteration
img = Image.open(self.images[self.index]).convert('RGB')
name = os.path.split(self.images[self.index])[-1]
# name = os.path.splitext(name)[0]
self.index += 1
return img, name
def __len__(self):
return self.size
class VideoLoader:
def __init__(self, root):
if os.path.isdir(root):
self.videos = [os.path.join(root, f) for f in os.listdir(root) if f.lower().endswith(('.mp4', '.avi', 'mov'))]
elif os.path.isfile(root):
self.videos = [root]
self.size = len(self.videos)
def __iter__(self):
self.index = 0
self.cap = None
self.fps = None
return self
def __next__(self):
if self.index == self.size:
raise StopIteration
if self.cap is None:
self.cap = cv2.VideoCapture(self.videos[self.index])
self.fps = self.cap.get(cv2.CAP_PROP_FPS)
ret, frame = self.cap.read()
name = os.path.split(self.videos[self.index])[-1]
# name = os.path.splitext(name)[0]
if ret is False:
self.cap.release()
self.cap = None
img = None
self.index += 1
else:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = Image.fromarray(frame).convert('RGB')
return img, name
def __len__(self):
return self.size
class WebcamLoader:
def __init__(self, ID):
self.ID = int(ID)
self.cap = cv2.VideoCapture(self.ID)
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
self.imgs = []
self.imgs.append(self.cap.read()[1])
self.thread = Thread(target=self.update, daemon=True)
self.thread.start()
def update(self):
while self.cap.isOpened():
ret, frame = self.cap.read()
if ret is True:
self.imgs.append(frame)
else:
break
def __iter__(self):
return self
def __next__(self):
if len(self.imgs) > 0:
frame = self.imgs[-1]
else:
frame = Image.fromarray(np.zeros((480, 640, 3)).astype(np.uint8))
if self.thread.is_alive() is False or cv2.waitKey(1) == ord('q'):
cv2.destroyAllWindows()
raise StopIteration
else:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame).convert('RGB')
del self.imgs[:-1]
return frame, None
def __len__(self):
return 0
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