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import os
import torch
import numpy as np
import torchvision
from urllib.request import urlopen
from PIL import Image, ImageDraw, ImageFont
from torchvision.transforms.functional import InterpolationMode
import torchvision.transforms as transforms
from decord import VideoReader
def get_font():
truetype_url = 'https://huggingface.co/internlm/internlm-xcomposer2d5-7b/resolve/main/SimHei.ttf?download=true'
ff = urlopen(truetype_url)
font = ImageFont.truetype(ff, size=40)
return font
def padding_336(b, pad=336):
width, height = b.size
tar = int(np.ceil(height / pad) * pad)
top_padding = 0 # int((tar - height)/2)
bottom_padding = tar - height - top_padding
left_padding = 0
right_padding = 0
b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
return b
def Image_transform(img, hd_num=25):
width, height = img.size
trans = False
if width < height:
img = img.transpose(Image.TRANSPOSE)
trans = True
width, height = img.size
ratio = (width / height)
scale = 1
while scale * np.ceil(scale / ratio) <= hd_num:
scale += 1
scale -= 1
scale = min(np.ceil(width / 560), scale)
new_w = int(scale * 560)
new_h = int(new_w / ratio)
#print (scale, f'{height}/{new_h}, {width}/{new_w}')
img = transforms.functional.resize(img, [new_h, new_w], )
img = padding_336(img, 560)
width, height = img.size
if trans:
img = img.transpose(Image.TRANSPOSE)
return img
def Video_transform(img, hd_num=25):
width, height = img.size
trans = False
if width < height:
img = img.transpose(Image.TRANSPOSE)
trans = True
width, height = img.size
ratio = (width/ height)
scale = 1
new_h = int(scale * 560)
new_w = int(new_h * ratio)
#print (new_h, new_w)
img = transforms.functional.resize(img, [new_h, new_w],)
img = img.transpose(Image.TRANSPOSE)
img = padding_336(img, 560)
width, height = img.size
if not trans:
img = img.transpose(Image.TRANSPOSE)
return img
def frame2img(imgs, font):
new_imgs = []
for img in imgs:
w, h = img.size
scale = w/h
if w > h:
new_w = 560 * 2
new_h = int(560 * 2 / scale)
else:
new_w = int(560 * 2 * scale)
new_h = 560 * 2
img = transforms.functional.resize(img, [new_h, new_w],)
new_imgs.append(img)
imgs = new_imgs
new_w = 0
new_h = 0
pad = 40
if w > h:
for im in imgs:
w,h = im.size
new_w = max(new_w, w)
new_h += h + 10 + pad
new_img = Image.new('RGB', (new_w, new_h), 'white')
draw = ImageDraw.Draw(new_img)
curr_h = 0
for idx, im in enumerate(imgs):
w,h = im.size
new_img.paste(im, (0, pad + curr_h))
draw.text((0, curr_h ), f'<IMAGE {idx}>', font=font, fill='black')
if idx + 1 < len(imgs):
draw.line([(0, pad +curr_h + h +5), (new_w, pad +curr_h + h +5)], fill = 'black', width=2)
curr_h += h + 10 + pad
#print (new_w, new_h)
else:
for im in imgs:
w,h = im.size
new_w += w + 10
new_h = max(new_h, h)
new_h += pad
new_img = Image.new('RGB', (new_w, new_h), 'white')
draw = ImageDraw.Draw(new_img)
curr_w = 0
for idx, im in enumerate(imgs):
w,h = im.size
new_img.paste(im, (curr_w, pad))
draw.text((curr_w, 0), f'<IMAGE {idx}>', font=font, fill='black')
if idx + 1 < len(imgs):
draw.line([(curr_w + w + 5, 0), (curr_w + w + 5, new_h)], fill = 'black', width=2)
curr_w += w + 10
return new_img
def load_video(video_path, num_frm=32, start=None, end=None):
vid = VideoReader(video_path, num_threads=1)
fps = vid.get_avg_fps()
t_stride = int(round(float(fps) / int(1)))
start_idx = 0 if start is None else start
end_idx = len(vid) if end is None else end
all_pos = list(range(start_idx, end_idx, t_stride))
try:
images = [vid[i].numpy() for i in all_pos]
except:
images = [vid[i].asnumpy() for i in all_pos]
if len(images) > num_frm:
num_frm = min(num_frm, len(images))
step_size = len(images) / (num_frm + 1)
indices = [int(i*step_size) for i in range(num_frm)]
images = [images[i] for i in indices]
images = [Image.fromarray(arr) for arr in images]
return images
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