mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-02-21 09:14:49 +08:00
254 lines
8.8 KiB
Python
254 lines
8.8 KiB
Python
import matplotlib.patches as patches
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from matplotlib.path import Path
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import os
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import sys
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import io
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import cv2
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import time
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import argparse
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import shutil
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import random
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import zipfile
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from glob import glob
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import math
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import numpy as np
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from PIL import Image, ImageOps, ImageDraw, ImageFilter
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import torch
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import torchvision
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import torch.nn as nn
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import torch.distributed as dist
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import matplotlib
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from matplotlib import pyplot as plt
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matplotlib.use('agg')
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# #####################################################
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# #####################################################
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class ZipReader(object):
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file_dict = dict()
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def __init__(self):
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super(ZipReader, self).__init__()
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@staticmethod
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def build_file_dict(path):
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file_dict = ZipReader.file_dict
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if path in file_dict:
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return file_dict[path]
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else:
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file_handle = zipfile.ZipFile(path, 'r')
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file_dict[path] = file_handle
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return file_dict[path]
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@staticmethod
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def imread(path, image_name):
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zfile = ZipReader.build_file_dict(path)
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data = zfile.read(image_name)
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im = Image.open(io.BytesIO(data))
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return im
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# ###########################################################################
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# ###########################################################################
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class GroupRandomHorizontalFlip(object):
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"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
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"""
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def __init__(self, is_flow=False):
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self.is_flow = is_flow
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def __call__(self, img_group, is_flow=False):
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v = random.random()
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if v < 0.5:
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ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
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if self.is_flow:
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for i in range(0, len(ret), 2):
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# invert flow pixel values when flipping
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ret[i] = ImageOps.invert(ret[i])
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return ret
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else:
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return img_group
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class Stack(object):
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def __init__(self, roll=False):
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self.roll = roll
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def __call__(self, img_group):
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mode = img_group[0].mode
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if mode == '1':
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img_group = [img.convert('L') for img in img_group]
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mode = 'L'
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if mode == 'L':
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return np.stack([np.expand_dims(x, 2) for x in img_group], axis=2)
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elif mode == 'RGB':
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if self.roll:
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return np.stack([np.array(x)[:, :, ::-1] for x in img_group], axis=2)
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else:
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return np.stack(img_group, axis=2)
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else:
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raise NotImplementedError(f"Image mode {mode}")
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class ToTorchFormatTensor(object):
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""" Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
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to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
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def __init__(self, div=True):
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self.div = div
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def __call__(self, pic):
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if isinstance(pic, np.ndarray):
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# numpy img: [L, C, H, W]
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img = torch.from_numpy(pic).permute(2, 3, 0, 1).contiguous()
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else:
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# handle PIL Image
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img = torch.ByteTensor(
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torch.ByteStorage.from_buffer(pic.tobytes()))
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img = img.view(pic.size[1], pic.size[0], len(pic.mode))
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# put it from HWC to CHW format
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# yikes, this transpose takes 80% of the loading time/CPU
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img = img.transpose(0, 1).transpose(0, 2).contiguous()
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img = img.float().div(255) if self.div else img.float()
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return img
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# ##########################################
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# ##########################################
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def create_random_shape_with_random_motion(video_length, imageHeight=240, imageWidth=432):
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# get a random shape
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height = random.randint(imageHeight//3, imageHeight-1)
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width = random.randint(imageWidth//3, imageWidth-1)
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edge_num = random.randint(6, 8)
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ratio = random.randint(6, 8)/10
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region = get_random_shape(
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edge_num=edge_num, ratio=ratio, height=height, width=width)
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region_width, region_height = region.size
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# get random position
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x, y = random.randint(
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0, imageHeight-region_height), random.randint(0, imageWidth-region_width)
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velocity = get_random_velocity(max_speed=3)
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m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8))
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m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
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masks = [m.convert('L')]
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# return fixed masks
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if random.uniform(0, 1) > 0.5:
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return masks*video_length
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# return moving masks
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for _ in range(video_length-1):
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x, y, velocity = random_move_control_points(
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x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3)
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m = Image.fromarray(
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np.zeros((imageHeight, imageWidth)).astype(np.uint8))
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m.paste(region, (y, x, y+region.size[0], x+region.size[1]))
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masks.append(m.convert('L'))
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return masks
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def get_random_shape(edge_num=9, ratio=0.7, width=432, height=240):
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'''
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There is the initial point and 3 points per cubic bezier curve.
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Thus, the curve will only pass though n points, which will be the sharp edges.
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The other 2 modify the shape of the bezier curve.
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edge_num, Number of possibly sharp edges
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points_num, number of points in the Path
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ratio, (0, 1) magnitude of the perturbation from the unit circle,
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'''
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points_num = edge_num*3 + 1
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angles = np.linspace(0, 2*np.pi, points_num)
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codes = np.full(points_num, Path.CURVE4)
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codes[0] = Path.MOVETO
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# Using this instad of Path.CLOSEPOLY avoids an innecessary straight line
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verts = np.stack((np.cos(angles), np.sin(angles))).T * \
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(2*ratio*np.random.random(points_num)+1-ratio)[:, None]
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verts[-1, :] = verts[0, :]
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path = Path(verts, codes)
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# draw paths into images
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fig = plt.figure()
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ax = fig.add_subplot(111)
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patch = patches.PathPatch(path, facecolor='black', lw=2)
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ax.add_patch(patch)
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ax.set_xlim(np.min(verts)*1.1, np.max(verts)*1.1)
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ax.set_ylim(np.min(verts)*1.1, np.max(verts)*1.1)
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ax.axis('off') # removes the axis to leave only the shape
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fig.canvas.draw()
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# convert plt images into numpy images
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data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
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data = data.reshape((fig.canvas.get_width_height()[::-1] + (3,)))
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plt.close(fig)
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# postprocess
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data = cv2.resize(data, (width, height))[:, :, 0]
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data = (1 - np.array(data > 0).astype(np.uint8))*255
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corrdinates = np.where(data > 0)
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xmin, xmax, ymin, ymax = np.min(corrdinates[0]), np.max(
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corrdinates[0]), np.min(corrdinates[1]), np.max(corrdinates[1])
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region = Image.fromarray(data).crop((ymin, xmin, ymax, xmax))
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return region
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def random_accelerate(velocity, maxAcceleration, dist='uniform'):
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speed, angle = velocity
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d_speed, d_angle = maxAcceleration
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if dist == 'uniform':
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speed += np.random.uniform(-d_speed, d_speed)
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angle += np.random.uniform(-d_angle, d_angle)
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elif dist == 'guassian':
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speed += np.random.normal(0, d_speed / 2)
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angle += np.random.normal(0, d_angle / 2)
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else:
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raise NotImplementedError(
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f'Distribution type {dist} is not supported.')
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return (speed, angle)
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def get_random_velocity(max_speed=3, dist='uniform'):
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if dist == 'uniform':
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speed = np.random.uniform(max_speed)
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elif dist == 'guassian':
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speed = np.abs(np.random.normal(0, max_speed / 2))
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else:
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raise NotImplementedError(
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f'Distribution type {dist} is not supported.')
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angle = np.random.uniform(0, 2 * np.pi)
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return (speed, angle)
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def random_move_control_points(X, Y, imageHeight, imageWidth, lineVelocity, region_size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3):
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region_width, region_height = region_size
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speed, angle = lineVelocity
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X += int(speed * np.cos(angle))
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Y += int(speed * np.sin(angle))
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lineVelocity = random_accelerate(
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lineVelocity, maxLineAcceleration, dist='guassian')
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if ((X > imageHeight - region_height) or (X < 0) or (Y > imageWidth - region_width) or (Y < 0)):
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lineVelocity = get_random_velocity(maxInitSpeed, dist='guassian')
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new_X = np.clip(X, 0, imageHeight - region_height)
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new_Y = np.clip(Y, 0, imageWidth - region_width)
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return new_X, new_Y, lineVelocity
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# ##############################################
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# ##############################################
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if __name__ == '__main__':
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trials = 10
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for _ in range(trials):
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video_length = 10
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# The returned masks are either stationary (50%) or moving (50%)
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masks = create_random_shape_with_random_motion(
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video_length, imageHeight=240, imageWidth=432)
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for m in masks:
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cv2.imshow('mask', np.array(m))
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cv2.waitKey(500)
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