mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-02-04 04:34:41 +08:00
272 lines
9.5 KiB
Python
272 lines
9.5 KiB
Python
import os
|
|
|
|
import matplotlib.patches as patches
|
|
from matplotlib.path import Path
|
|
import io
|
|
import cv2
|
|
import random
|
|
import zipfile
|
|
import numpy as np
|
|
from PIL import Image, ImageOps
|
|
import torch
|
|
import matplotlib
|
|
from matplotlib import pyplot as plt
|
|
matplotlib.use('agg')
|
|
|
|
|
|
class ZipReader(object):
|
|
file_dict = dict()
|
|
|
|
def __init__(self):
|
|
super(ZipReader, self).__init__()
|
|
|
|
@staticmethod
|
|
def build_file_dict(path):
|
|
file_dict = ZipReader.file_dict
|
|
if path in file_dict:
|
|
return file_dict[path]
|
|
else:
|
|
file_handle = zipfile.ZipFile(path, 'r')
|
|
file_dict[path] = file_handle
|
|
return file_dict[path]
|
|
|
|
@staticmethod
|
|
def imread(path, image_name):
|
|
zfile = ZipReader.build_file_dict(path)
|
|
data = zfile.read(image_name)
|
|
im = Image.open(io.BytesIO(data))
|
|
return im
|
|
|
|
|
|
class GroupRandomHorizontalFlip(object):
|
|
"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
|
|
"""
|
|
|
|
def __init__(self, is_flow=False):
|
|
self.is_flow = is_flow
|
|
|
|
def __call__(self, img_group, is_flow=False):
|
|
v = random.random()
|
|
if v < 0.5:
|
|
ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
|
|
if self.is_flow:
|
|
for i in range(0, len(ret), 2):
|
|
# invert flow pixel values when flipping
|
|
ret[i] = ImageOps.invert(ret[i])
|
|
return ret
|
|
else:
|
|
return img_group
|
|
|
|
|
|
class Stack(object):
|
|
def __init__(self, roll=False):
|
|
self.roll = roll
|
|
|
|
def __call__(self, img_group):
|
|
mode = img_group[0].mode
|
|
if mode == '1':
|
|
img_group = [img.convert('L') for img in img_group]
|
|
mode = 'L'
|
|
if mode == 'L':
|
|
return np.stack([np.expand_dims(x, 2) for x in img_group], axis=2)
|
|
elif mode == 'RGB':
|
|
if self.roll:
|
|
return np.stack([np.array(x)[:, :, ::-1] for x in img_group], axis=2)
|
|
else:
|
|
return np.stack(img_group, axis=2)
|
|
else:
|
|
raise NotImplementedError(f"Image mode {mode}")
|
|
|
|
|
|
class ToTorchFormatTensor(object):
|
|
""" Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
|
|
to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
|
|
|
|
def __init__(self, div=True):
|
|
self.div = div
|
|
|
|
def __call__(self, pic):
|
|
if isinstance(pic, np.ndarray):
|
|
# numpy img: [L, C, H, W]
|
|
img = torch.from_numpy(pic).permute(2, 3, 0, 1).contiguous()
|
|
else:
|
|
# handle PIL Image
|
|
img = torch.ByteTensor(
|
|
torch.ByteStorage.from_buffer(pic.tobytes()))
|
|
img = img.view(pic.size[1], pic.size[0], len(pic.mode))
|
|
# put it from HWC to CHW format
|
|
# yikes, this transpose takes 80% of the loading time/CPU
|
|
img = img.transpose(0, 1).transpose(0, 2).contiguous()
|
|
img = img.float().div(255) if self.div else img.float()
|
|
return img
|
|
|
|
|
|
def create_random_shape_with_random_motion(video_length, imageHeight=240, imageWidth=432):
|
|
# get a random shape
|
|
height = random.randint(imageHeight//3, imageHeight-1)
|
|
width = random.randint(imageWidth//3, imageWidth-1)
|
|
edge_num = random.randint(6, 8)
|
|
ratio = random.randint(6, 8)/10
|
|
region = get_random_shape(
|
|
edge_num=edge_num, ratio=ratio, height=height, width=width)
|
|
region_width, region_height = region.size
|
|
# get random position
|
|
x, y = random.randint(
|
|
0, imageHeight-region_height), random.randint(0, imageWidth-region_width)
|
|
velocity = get_random_velocity(max_speed=3)
|
|
m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8))
|
|
m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
|
|
masks = [m.convert('L')]
|
|
# return fixed masks
|
|
if random.uniform(0, 1) > 0.5:
|
|
return masks*video_length
|
|
# return moving masks
|
|
for _ in range(video_length-1):
|
|
x, y, velocity = random_move_control_points(
|
|
x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3)
|
|
m = Image.fromarray(
|
|
np.zeros((imageHeight, imageWidth)).astype(np.uint8))
|
|
m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
|
|
masks.append(m.convert('L'))
|
|
return masks
|
|
|
|
|
|
def get_random_shape(edge_num=9, ratio=0.7, width=432, height=240):
|
|
'''
|
|
There is the initial point and 3 points per cubic bezier curve.
|
|
Thus, the curve will only pass though n points, which will be the sharp edges.
|
|
The other 2 modify the shape of the bezier curve.
|
|
edge_num, Number of possibly sharp edges
|
|
points_num, number of points in the Path
|
|
ratio, (0, 1) magnitude of the perturbation from the unit circle,
|
|
'''
|
|
points_num = edge_num*3 + 1
|
|
angles = np.linspace(0, 2*np.pi, points_num)
|
|
codes = np.full(points_num, Path.CURVE4)
|
|
codes[0] = Path.MOVETO
|
|
# Using this instad of Path.CLOSEPOLY avoids an innecessary straight line
|
|
verts = np.stack((np.cos(angles), np.sin(angles))).T * \
|
|
(2*ratio*np.random.random(points_num)+1-ratio)[:, None]
|
|
verts[-1, :] = verts[0, :]
|
|
path = Path(verts, codes)
|
|
# draw paths into images
|
|
fig = plt.figure()
|
|
ax = fig.add_subplot(111)
|
|
patch = patches.PathPatch(path, facecolor='black', lw=2)
|
|
ax.add_patch(patch)
|
|
ax.set_xlim(np.min(verts)*1.1, np.max(verts)*1.1)
|
|
ax.set_ylim(np.min(verts)*1.1, np.max(verts)*1.1)
|
|
ax.axis('off') # removes the axis to leave only the shape
|
|
fig.canvas.draw()
|
|
# convert plt images into numpy images
|
|
data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
|
|
data = data.reshape((fig.canvas.get_width_height()[::-1] + (3,)))
|
|
plt.close(fig)
|
|
# postprocess
|
|
data = cv2.resize(data, (width, height))[:, :, 0]
|
|
data = (1 - np.array(data > 0).astype(np.uint8))*255
|
|
corrdinates = np.where(data > 0)
|
|
xmin, xmax, ymin, ymax = np.min(corrdinates[0]), np.max(
|
|
corrdinates[0]), np.min(corrdinates[1]), np.max(corrdinates[1])
|
|
region = Image.fromarray(data).crop((ymin, xmin, ymax, xmax))
|
|
return region
|
|
|
|
|
|
def random_accelerate(velocity, maxAcceleration, dist='uniform'):
|
|
speed, angle = velocity
|
|
d_speed, d_angle = maxAcceleration
|
|
if dist == 'uniform':
|
|
speed += np.random.uniform(-d_speed, d_speed)
|
|
angle += np.random.uniform(-d_angle, d_angle)
|
|
elif dist == 'guassian':
|
|
speed += np.random.normal(0, d_speed / 2)
|
|
angle += np.random.normal(0, d_angle / 2)
|
|
else:
|
|
raise NotImplementedError(
|
|
f'Distribution type {dist} is not supported.')
|
|
return (speed, angle)
|
|
|
|
|
|
def get_random_velocity(max_speed=3, dist='uniform'):
|
|
if dist == 'uniform':
|
|
speed = np.random.uniform(max_speed)
|
|
elif dist == 'guassian':
|
|
speed = np.abs(np.random.normal(0, max_speed / 2))
|
|
else:
|
|
raise NotImplementedError(
|
|
f'Distribution type {dist} is not supported.')
|
|
angle = np.random.uniform(0, 2 * np.pi)
|
|
return (speed, angle)
|
|
|
|
|
|
def random_move_control_points(X, Y, imageHeight, imageWidth, lineVelocity, region_size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3):
|
|
region_width, region_height = region_size
|
|
speed, angle = lineVelocity
|
|
X += int(speed * np.cos(angle))
|
|
Y += int(speed * np.sin(angle))
|
|
lineVelocity = random_accelerate(
|
|
lineVelocity, maxLineAcceleration, dist='guassian')
|
|
if (X > imageHeight - region_height) or (X < 0) or (Y > imageWidth - region_width) or (Y < 0):
|
|
lineVelocity = get_random_velocity(maxInitSpeed, dist='guassian')
|
|
new_X = np.clip(X, 0, imageHeight - region_height)
|
|
new_Y = np.clip(Y, 0, imageWidth - region_width)
|
|
return new_X, new_Y, lineVelocity
|
|
|
|
|
|
def get_world_size():
|
|
"""Find OMPI world size without calling mpi functions
|
|
:rtype: int
|
|
"""
|
|
if os.environ.get('PMI_SIZE') is not None:
|
|
return int(os.environ.get('PMI_SIZE') or 1)
|
|
elif os.environ.get('OMPI_COMM_WORLD_SIZE') is not None:
|
|
return int(os.environ.get('OMPI_COMM_WORLD_SIZE') or 1)
|
|
else:
|
|
return torch.cuda.device_count()
|
|
|
|
|
|
def get_global_rank():
|
|
"""Find OMPI world rank without calling mpi functions
|
|
:rtype: int
|
|
"""
|
|
if os.environ.get('PMI_RANK') is not None:
|
|
return int(os.environ.get('PMI_RANK') or 0)
|
|
elif os.environ.get('OMPI_COMM_WORLD_RANK') is not None:
|
|
return int(os.environ.get('OMPI_COMM_WORLD_RANK') or 0)
|
|
else:
|
|
return 0
|
|
|
|
|
|
def get_local_rank():
|
|
"""Find OMPI local rank without calling mpi functions
|
|
:rtype: int
|
|
"""
|
|
if os.environ.get('MPI_LOCALRANKID') is not None:
|
|
return int(os.environ.get('MPI_LOCALRANKID') or 0)
|
|
elif os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') is not None:
|
|
return int(os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') or 0)
|
|
else:
|
|
return 0
|
|
|
|
|
|
def get_master_ip():
|
|
if os.environ.get('AZ_BATCH_MASTER_NODE') is not None:
|
|
return os.environ.get('AZ_BATCH_MASTER_NODE').split(':')[0]
|
|
elif os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE') is not None:
|
|
return os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE')
|
|
else:
|
|
return "127.0.0.1"
|
|
|
|
if __name__ == '__main__':
|
|
trials = 10
|
|
for _ in range(trials):
|
|
video_length = 10
|
|
# The returned masks are either stationary (50%) or moving (50%)
|
|
masks = create_random_shape_with_random_motion(
|
|
video_length, imageHeight=240, imageWidth=432)
|
|
|
|
for m in masks:
|
|
cv2.imshow('mask', np.array(m))
|
|
cv2.waitKey(500)
|
|
|