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# System Imports
import os
import math
import argparse
import time
# Common Libs
import numpy as np
from pathlib import Path
import cv2
import tkinter as tk
import threading
import queue
# Torch Imports
import torch
import torch.nn.functional as F
from torchvision import transforms
from torchvision.utils import save_image
# 3rd party imports
from transformers import DPTForDepthEstimation, DPTImageProcessor
from tqdm import tqdm
import mediapipe as mp
from PIL import Image, ImageTk
from moviepy.editor import ImageSequenceClip
# From Codebase
from utils.mpi import mpi_rendering
from utils.mpi.homography_sampler import HomographySample
from utils.mpi.homography_sampler import HomographySample
from utils.utils import (
image_to_tensor,
disparity_to_tensor,
render_3dphoto,
render_novel_view,
)
from model.AdaMPI import MPIPredictor
from parameters import *
#=================================================
# Define the MPI Layers Processing Module Here
#=================================================
def processMPIs(src_imgs, mpi_all_src, disparity_all_src, k_src, k_tgt, save_path=None):
h, w = mpi_all_src.shape[-2:]
device = mpi_all_src.device
homography_sampler = HomographySample(h, w, device)
k_src_inv = torch.inverse(k_src)
# preprocess the predict MPI
xyz_src_BS3HW = mpi_rendering.get_src_xyz_from_plane_disparity(
homography_sampler.meshgrid,
disparity_all_src,
k_src_inv,
)
mpi_all_rgb_src = mpi_all_src[:, :, 0:3, :, :] # BxSx3xHxW
mpi_all_sigma_src = mpi_all_src[:, :, 3:, :, :] # BxSx1xHxW
_, _, blend_weights, _ = mpi_rendering.render(
mpi_all_rgb_src,
mpi_all_sigma_src,
xyz_src_BS3HW,
use_alpha=False,
is_bg_depth_inf=False,
)
mpi_all_rgb_src = blend_weights * src_imgs.unsqueeze(1) + (1 - blend_weights) * mpi_all_rgb_src
return mpi_all_rgb_src, mpi_all_sigma_src, disparity_all_src, k_src_inv,k_tgt,homography_sampler
def cropFOV(image, original_fov, new_fov):
image = np.array(image)
if new_fov >= original_fov:
raise ValueError("New FoV must be smaller than the original FoV")
crop_ratio = new_fov / original_fov
height, width = image.shape[:2]
new_width = int(width * crop_ratio)
new_height = int(height * crop_ratio)
start_x = (width - new_width) // 2
start_y = (height - new_height) // 2
cropped_image = image[start_y:start_y + new_height, start_x:start_x + new_width]
cropped_image = Image.fromarray(cropped_image)
return cropped_image
def renderSingleFrame(mpi_all_rgb_src, mpi_all_sigma_src, disparity_all_src, cam_ext, k_src_inv, k_tgt, homography_sampler):
frame = render_novel_view(
mpi_all_rgb_src,
mpi_all_sigma_src,
disparity_all_src,
cam_ext.to(device),
k_src_inv,
k_tgt,
homography_sampler,
)
frame_np = frame[0].permute(1, 2, 0).contiguous().cpu().numpy() # [b,h,w,3]
frame_np = np.clip(np.round(frame_np * 255), a_min=0, a_max=255).astype(np.uint8)
im = Image.fromarray(frame_np)
return im
class VideoCapture:
def __init__(self, name):
self.cap = cv2.VideoCapture(name)
self.q = queue.Queue()
t = threading.Thread(target=self._reader)
t.daemon = True
t.start()
def _reader(self):
while True:
ret, frame = self.cap.read()
if not ret:
break
if not self.q.empty():
try:
self.q.get_nowait()
except queue.Empty:
pass
self.q.put(frame)
def read(self):
return self.q.get()
def captureBackground(capture_device):
frame_background = capture_device.read()
img = cv2.cvtColor(frame_background, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(img)
return im_pil
def getImageTensor(pil_image, height, width, unsqueeze=True):
t = transforms.Compose([transforms.CenterCrop((height, width)),transforms.ToTensor()])
rgb = t(pil_image)
if unsqueeze:
rgb = rgb.unsqueeze(0)
return rgb |