File size: 3,901 Bytes
0ac7e98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import torchvision
from PIL import Image, ImageDraw
import imageio
import cv2
import torch
import numpy as np
import zipfile
def render_video(tensor_fgr,
tensor_pha,
nrow=8,
normalize=True,
value_range=(-1, 1)):
def to_tensor(arr_list):
tensor_list= [torch.from_numpy(arr).float().div_(127.5).sub_(1) for arr in arr_list]
tensor_list = torch.stack(tensor_list, dim = 0).permute(3,0,1,2).unsqueeze(0)
return tensor_list
if not torch.is_tensor(tensor_fgr):
tensor_fgr = to_tensor(tensor_fgr)
if not torch.is_tensor(tensor_pha):
tensor_pha = to_tensor(tensor_pha)
tensor_fgr = tensor_fgr.clamp(min(value_range), max(value_range))
tensor_fgr = torch.stack([
torchvision.utils.make_grid(
u, nrow=nrow, normalize=normalize, value_range=value_range)
for u in tensor_fgr.unbind(2)
],
dim=1).permute(1, 2, 3, 0)
tensor_fgr = (tensor_fgr * 255).type(torch.uint8).cpu()
tensor_pha = tensor_pha.clamp(min(value_range), max(value_range))
tensor_pha = torch.stack([
torchvision.utils.make_grid(
u, nrow=nrow, normalize=normalize, value_range=value_range)
for u in tensor_pha.unbind(2)
],
dim=1).permute(1, 2, 3, 0)
tensor_pha = (tensor_pha * 255).type(torch.uint8).cpu()
frames = []
frames_fgr = []
frames_pha = []
for frame_fgr, frame_pha in zip(tensor_fgr.numpy(), tensor_pha.numpy()):
if frame_pha.shape[-1] == 1:
frame_pha = frame_pha[:,:,0]
else:
frame_pha = (0.0 + frame_pha[:,:,0:1] + frame_pha[:,:,1:2] + frame_pha[:,:,2:3]) / 3.
frame = np.concatenate([frame_fgr[:,:,::-1], frame_pha.astype(np.uint8)], axis=2)
frames.append(frame)
frames_fgr.append(frame_fgr)
frames_pha.append(frame_pha)
def create_checkerboard(size=30, pattern_size=(830, 480), color1=(140, 140, 140), color2=(113, 113, 113)):
img = Image.new('RGB', (pattern_size[0], pattern_size[1]), color1)
draw = ImageDraw.Draw(img)
for i in range(0, pattern_size[0], size):
for j in range(0, pattern_size[1], size):
if (i + j) // size % 2 == 0:
draw.rectangle([i, j, i+size, j+size], fill=color2)
return img
def blender_background(frame_rgba, checkerboard):
alpha_channel = frame_rgba[:, :, 3:] / 255.
checkerboard = np.array(checkerboard)
checkerboard = cv2.resize(checkerboard, (frame_rgba.shape[1], frame_rgba.shape[0]))
frame_rgb = frame_rgba[:, :, :3] * alpha_channel + checkerboard * (1-alpha_channel)
return frame_rgb.astype(np.uint8)[:,:,::-1]
checkerboard = create_checkerboard()
video_checkerboard = [torch.from_numpy(blender_background(f, checkerboard).copy()).float().div_(127.5).sub_(1) for f in frames]
video_checkerboard = torch.stack(video_checkerboard ).permute(3, 0, 1, 2)
return video_checkerboard, frames
def from_BRGA_numpy_to_RGBA_torch(video):
video = [torch.from_numpy(f.copy()).float().div_(127.5).sub_(1) for f in video]
video = torch.stack(video).permute(3, 0, 1, 2)
video[[0, 2], ...] = video[[2, 0], ...]
return video
def write_zip_file(zip_path, frames):
# frames in BGRA format
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
for idx, img in enumerate(frames):
success, buffer = cv2.imencode(".png", img)
if not success:
print(f"Failed to encode image {idx}, skipping...")
continue
filename = f"img_{idx:03d}.png"
zipf.writestr(filename, buffer.tobytes()) |