import argparse
import math
import os
import cv2
import subprocess
from datetime import timedelta
from urllib.parse import urlparse
import re
import numpy as np
import PIL
from PIL import Image, ImageDraw
import datetime
import torch
import torchvision
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
import random
from skimage.metrics import structural_similarity as compare_ssim
from diffusers.utils import load_image
def export_to_video(video_frames, output_video_path, fps):
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
h, w, _ = video_frames[0].shape
video_writer = cv2.VideoWriter(
output_video_path, fourcc, fps=fps, frameSize=(w, h))
for i in range(len(video_frames)):
img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
video_writer.write(img)
def export_to_gif(frames, output_gif_path, fps):
"""
Export a list of frames to a GIF.
Args:
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- output_gif_path (str): Path to save the output GIF.
- duration_ms (int): Duration of each frame in milliseconds.
"""
# Convert numpy arrays to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(
frame, np.ndarray) else frame for frame in frames]
pil_frames[0].save(output_gif_path.replace('.mp4', '.gif'),
format='GIF',
append_images=pil_frames[1:],
save_all=True,
duration=100,
loop=0)
from PIL import Image
import numpy as np
def export_gif_with_ref(start_image, frames, end_image, reference_image, output_gif_path, fps):
"""
Export a list of frames into a GIF with columns and an additional version with only frames.
Args:
- start_image (PIL.Image): The starting image.
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- end_image (PIL.Image): The ending image.
- reference_image (PIL.Image): The reference image.
- output_gif_path (str): Path to save the output GIF.
- fps (int): Frames per second for the GIF.
"""
# Convert numpy frames to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
# Get dimensions of images
width, height = start_image.size
# Resize the reference image and frames to match the height of start and end images if needed
reference_image = reference_image.resize((reference_image.width, height))
resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames]
# Create a new image for each frame with the three columns
column_frames = []
for frame in resized_frames:
# Create an empty image with the total width for all three columns
new_width = start_image.width + reference_image.width + end_image.width+frame.width
combined_frame = Image.new('RGB', (new_width, height))
# Paste the start image, reference image, and frame into the new image
combined_frame.paste(start_image, (0, 0))
combined_frame.paste(reference_image, (start_image.width, 0))
combined_frame.paste(end_image, (start_image.width + reference_image.width, 0))
combined_frame.paste(frame, (start_image.width + reference_image.width+end_image.width, 0))
column_frames.append(combined_frame)
# Calculate frame duration in milliseconds based on fps
frame_duration = 150
# Save the GIF with columns
column_frames[0].save(output_gif_path,
format='GIF',
append_images=column_frames[1:],
save_all=True,
duration=frame_duration,
loop=0)
def tensor_to_vae_latent(t, vae):
video_length = t.shape[1]
t = rearrange(t, "b f c h w -> (b f) c h w")
latents = vae.encode(t).latent_dist.sample()
latents = rearrange(latents, "(b f) c h w -> b f c h w", f=video_length)
latents = latents * vae.config.scaling_factor
return latents
def download_image(url):
original_image = (
lambda image_url_or_path: load_image(image_url_or_path)
if urlparse(image_url_or_path).scheme
else PIL.Image.open(image_url_or_path).convert("RGB")
)(url)
return original_image
def map_ssim_distance(dis):
if dis > 0.95:
return 1
elif dis > 0.9:
return 2
elif dis > 0.85:
return 3
elif dis > 0.80:
return 4
elif dis > 0.75:
return 5
elif dis > 0.70:
return 6
elif dis > 0.65:
return 7
elif dis > 0.60:
return 8
elif dis > 0.55:
return 9
else:
return 10
def calculate_ssim(frame1, frame2):
# convert the frames to grayscale images since the compare_ssim function accepts grayscale images
gray_frame1 = cv2.cvtColor(frame1, cv2.COLOR_RGB2GRAY)
gray_frame2 = cv2.cvtColor(frame2, cv2.COLOR_RGB2GRAY)
# compute SSIM
ssim = compare_ssim(gray_frame1, gray_frame2)
return ssim
def mse(image1, image2):
err = np.sum((image1.astype("float") - image2.astype("float")) ** 2)
err /= float(image1.shape[0] * image1.shape[1])
return err
def calculate_video_motion_distance(frames_data):
# obtain the number of frames in the video
frame_count, _, _, _ = frames_data.shape
# init
similarities = []
# calculate the similarity between each two frames
for frame_index in range(1, frame_count):
prev_frame = frames_data[frame_index - 1, :, :, :]
current_frame = frames_data[frame_index, :, :, :]
# calculate the similarity, you can choose to use SSIM or MSE, etc.
similarity = calculate_ssim(prev_frame, current_frame)
similarities.append(similarity)
# calculate the mean similarity as the motion distance of the video
motion_distance = np.mean(similarities)
return similarities, motion_distance
def load_images_from_folder_to_pil(folder, target_size=(512, 512)):
images = []
valid_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"} # Add or remove extensions as needed
def frame_number(filename):
# Try the pattern 'frame_x_7fps'
new_pattern_match = re.search(r'frame_(\d+)_7fps', filename)
if new_pattern_match:
return int(new_pattern_match.group(1))
# If the new pattern is not found, use the original digit extraction method
matches = re.findall(r'\d+', filename)
if matches:
if matches[-1] == '0000' and len(matches) > 1:
return int(matches[-2]) # Return the second-to-last sequence if the last is '0000'
return int(matches[-1]) # Otherwise, return the last sequence
return float('inf') # Return 'inf'
# Sorting files based on frame number
# sorted_files = sorted(os.listdir(folder), key=frame_number)
sorted_files = sorted(os.listdir(folder))
# Load, resize, and convert images
for filename in sorted_files:
ext = os.path.splitext(filename)[1].lower()
if ext in valid_extensions:
img_path = os.path.join(folder, filename)
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # Read image with original channels
if img is not None:
# Resize image
img = cv2.resize(img, target_size, interpolation=cv2.INTER_AREA)
# Convert to uint8 if necessary
if img.dtype == np.uint16:
img = (img / 256).astype(np.uint8)
# Ensure all images are in RGB format
if len(img.shape) == 2: # Grayscale image
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
elif len(img.shape) == 3 and img.shape[2] == 3: # Color image in BGR format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Convert the numpy array to a PIL image
pil_img = Image.fromarray(img)
images.append(pil_img)
return images
def extract_frames_from_video(video_path):
video_capture = cv2.VideoCapture(video_path)
frames = []
if not video_capture.isOpened():
return frames
while True:
ret, frame = video_capture.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(frame_rgb)
frames.append(pil_image)
video_capture.release()
return frames
def export_gif_side_by_side(ref_frame,sketches, frames, output_gif_path, fps):
"""
Export a list of frames into a GIF with columns and an additional version with only frames.
Args:
- start_image (PIL.Image): The starting image.
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- end_image (PIL.Image): The ending image.
- reference_image (PIL.Image): The reference image.
- output_gif_path (str): Path to save the output GIF.
- fps (int): Frames per second for the GIF.
"""
# Convert numpy frames to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
# Get dimensions of images
width, height = pil_frames[0].size
resized_frames = [frame.resize((width, height)) for frame in pil_frames]
resized_sketches = [sketch.resize((width, height)) for sketch in sketches]
ref_frame=ref_frame.resize((width, height))
# Create a new image for each frame with the three columns
column_frames = []
for i, frame in enumerate(resized_frames):
# Create an empty image with the total width for all three columns
new_width = resized_sketches[0].width + frame.width+frame.width
combined_frame = Image.new('RGB', (new_width, height))
# Paste the start image, reference image, and frame into the new image
combined_frame.paste(ref_frame, (0, 0))
combined_frame.paste(resized_sketches[i], (resized_sketches[0].width, 0))
combined_frame.paste(frame, (resized_sketches[0].width+resized_sketches[0].width, 0))
column_frames.append(combined_frame)
# Calculate frame duration in milliseconds based on fps
frame_duration = 150
# Save the GIF with columns
column_frames[0].save(output_gif_path,
format='GIF',
append_images=column_frames[1:],
save_all=True,
duration=frame_duration,
loop=0)
#shuffle operation
def safe_round(coords, size):
height, width = size[1], size[2]
rounded_coords = np.round(coords).astype(int)
rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1)
rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1)
return rounded_coords
def random_number(num_points,size,coords0,coords1):
shuffle_indices = np.random.permutation(np.arange(coords0.shape[0]))
shuffled_coords0 = coords0[shuffle_indices]
shuffled_coords1 = coords1[shuffle_indices]
indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False)
# selected_coords0 = coords0[indices]
# selected_coords1 = coords1[indices]
selected_coords0 = shuffled_coords0[indices]
selected_coords1 = shuffled_coords1[indices]
h, w = size[1], size[2]
mask0 = np.zeros((h, w), dtype=np.uint8)
mask1 = np.zeros((h, w), dtype=np.uint8)
for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)):
x0, y0 = coord0
x1, y1 = coord1
# import ipdb;ipdb.set_trace()
mask0[y0, x0] = i + 1
mask1[y1, x1] = i + 1
return mask0,mask1
def split_and_shuffle(image, coordinates):
assert image.shape[1] % 2 == 0 and image.shape[2] % 2 == 0, "Height and width must be even."
H, W = image.shape[1], image.shape[2]
patches_img = [
image[:, :H//2, :W//2],
image[:, :H//2, W//2:],
image[:, H//2:, :W//2],
image[:, H//2:, W//2:]
]
patch_coords = [
(0, H//2, 0, W//2),
(0, H//2, W//2, W),
(H//2, H, 0, W//2),
(H//2, H, W//2, W)
]
indices = list(range(4))
random.shuffle(indices)
new_patch_coords = [
(0, 0),
(0, W//2),
(H//2, 0),
(H//2, W//2)
]
new_coordinates = np.zeros_like(coordinates)
for i, (r, c) in enumerate(coordinates):
for idx, (r1, r2, c1, c2) in enumerate(patch_coords):
if r1 <= r < r2 and c1 <= c < c2:
new_r = r - r1 + new_patch_coords[indices.index(idx)][0]
new_c = c - c1 + new_patch_coords[indices.index(idx)][1]
new_coordinates[i] = [new_r, new_c]
break
shuffled_img = torch.cat([
torch.cat([patches_img[indices[0]], patches_img[indices[1]]], dim=2),
torch.cat([patches_img[indices[2]], patches_img[indices[3]]], dim=2)
], dim=1)
return shuffled_img, new_coordinates
import os
import cv2
def extract_frames_from_videos(video_folder):
for filename in os.listdir(video_folder):
if filename.endswith('.mp4'):
video_path = os.path.join(video_folder, filename)
frames_folder = os.path.join("processed_video", os.path.splitext(filename)[0])
os.makedirs(frames_folder, exist_ok=True)
cap = cv2.VideoCapture(video_path)
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
frame_filename = os.path.join(frames_folder, f'frame_{frame_count:04d}.jpg')
cv2.imwrite(frame_filename, frame)
frame_count += 1
cap.release()
print(f'Extracted {frame_count} frames from {filename} and saved to {frames_folder}')
def create_videos_from_frames(base_folder, output_folder, frame_rate=30):
for root, dirs, files in os.walk(base_folder):
frames = []
for file in sorted(files):
if file.endswith(('.jpg', '.png')):
frame_path = os.path.join(root, file)
frames.append(frame_path)
if len(frames) == 14:
video_name = os.path.basename(root) + '.mp4'
video_path = os.path.join(output_folder, video_name)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
first_frame = cv2.imread(frames[0])
height, width, layers = first_frame.shape
video_writer = cv2.VideoWriter(video_path, fourcc, frame_rate, (width, height))
for frame in frames:
img = cv2.imread(frame)
video_writer.write(img)
video_writer.release()
print(f'Created video: {video_path}')
def random_rotate(image, angle_range=(-60, 60)):
angle = random.uniform(*angle_range)
return image.rotate(angle, fillcolor=(255, 255, 255))
def random_crop(image,ratio=0.9):
width, height = image.size
ratio = random.uniform(0.6, 1.0)
# print('ratio',ratio)
top = random.randint(0, height - int(height*ratio))
left = random.randint(0, width - int(width*ratio))
image=image.crop((left, top, left + int( width*ratio), top + int(height*ratio)))
image=image.resize((width,height))
return image
def random_flip(image):
if random.random() < 0.5:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
if random.random() < 0.5:
image = image.transpose(Image.FLIP_TOP_BOTTOM)
return image
def patch_shuffle(image, num_patches):
C, H, W = image.shape
assert H % num_patches == 0 and W % num_patches == 0, "Image dimensions must be divisible by num_patches"
patch_size_h = H // num_patches
patch_size_w = W // num_patches
patches = image.unfold(1, patch_size_h, patch_size_h).unfold(2, patch_size_w, patch_size_w)
patches = patches.contiguous().view(C, num_patches * num_patches, patch_size_h, patch_size_w)
shuffle_idx = torch.randperm(num_patches * num_patches)
shuffled_patches = patches[:, shuffle_idx, :, :]
shuffled_patches = shuffled_patches.view(C, num_patches, num_patches, patch_size_h, patch_size_w)
shuffled_image = shuffled_patches.permute(0, 1, 3, 2, 4).contiguous()
shuffled_image = shuffled_image.view(C, H, W)
return shuffled_image
def augment_image(image,k):
image = random_rotate(image)
image = random_crop(image)
image = random_flip(image)
# torch_image = torchvision.transforms.ToTensor()(image)
# patch_shuffled_image = patch_shuffle(torch_image, k)
# to_pil = transforms.ToPILImage()
# image = to_pil(patch_shuffled_image)
return image
def load_images_from_folder(folder):
image_list = []
for filename in os.listdir(folder):
if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".jpeg"):
img_path = os.path.join(folder, filename)
try:
img = Image.open(img_path)
image_list.append(img)
except Exception as e:
print(f"Error loading image {filename}: {e}")
return image_list
def get_mask(model, input_img, s=640):
input_img = (input_img / 255).astype(np.float32)
h, w = h0, w0 = input_img.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
img_input = np.zeros([s, s, 3], dtype=np.float32)
img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h))
img_input = np.transpose(img_input, (2, 0, 1))
img_input = img_input[np.newaxis, :]
tmpImg = torch.from_numpy(img_input).type(torch.FloatTensor).to(model.device)
with torch.no_grad():
pred = model(tmpImg)
pred = pred.cpu().numpy()[0]
pred = np.transpose(pred, (1, 2, 0))
pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis]
return pred
# code from
def safe_round(coords, size):
height, width = size[1], size[2]
rounded_coords = np.round(coords).astype(int)
rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1)
rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1)
return rounded_coords
def random_number(num_points,size,coords0,coords1):
shuffle_indices = np.random.permutation(np.arange(coords0.shape[0]))
shuffled_coords0 = coords0[shuffle_indices]
shuffled_coords1 = coords1[shuffle_indices]
indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False)
# selected_coords0 = coords0[indices]
# selected_coords1 = coords1[indices]
selected_coords0 = shuffled_coords0[indices]
selected_coords1 = shuffled_coords1[indices]
h, w = size[1], size[2]
mask0 = np.zeros((h, w), dtype=np.uint8)
mask1 = np.zeros((h, w), dtype=np.uint8)
for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)):
x0, y0 = coord0
x1, y1 = coord1
# import ipdb;ipdb.set_trace()
mask0[y0, x0] = i + 1
mask1[y1, x1] = i + 1
return mask0,mask1
import torch
def split_and_shuffle(image, keypoints, num_rows, num_cols):
"""
Split the image into tiles, shuffle them, and update the keypoints accordingly.
Parameters:
- image: Tensor of shape (3, H, W)
- keypoints: Tensor of shape (num_k, 2)
- num_rows: int, number of rows to split
- num_cols: int, number of columns to split
Returns:
- shuffled_image: Tensor of shape (3, H, W)
- new_keypoints: Tensor of shape (num_k, 2)
"""
C, H, W = image.shape
# Calculate padding to make H and W divisible by num_rows and num_cols
pad_h = (num_rows - H % num_rows) % num_rows
pad_w = (num_cols - W % num_cols) % num_cols
# Pad the image
H_padded = H + pad_h
W_padded = W + pad_w
padded_image = torch.zeros((C, H_padded, W_padded), dtype=image.dtype).to(image.device)
padded_image[:, :H, :W] = image
# Compute tile size
tile_height = H_padded // num_rows
tile_width = W_padded // num_cols
# Reshape and permute to get tiles
tiles = padded_image.reshape(C,
num_rows,
tile_height,
num_cols,
tile_width)
tiles = tiles.permute(1, 3, 0, 2, 4).contiguous()
num_tiles = num_rows * num_cols
tiles = tiles.view(num_tiles, C, tile_height, tile_width)
# Shuffle the tiles
idx_shuffle = torch.randperm(num_tiles).to(image.device)
tiles_shuffled = tiles[idx_shuffle]
# Reshape back to image
tiles_shuffled = tiles_shuffled.view(num_rows, num_cols, C, tile_height, tile_width)
shuffled_image = tiles_shuffled.permute(2, 0, 3, 1, 4).contiguous()
shuffled_image = shuffled_image.view(C, H_padded, W_padded)
shuffled_image = shuffled_image[:, :H, :W] # Crop back to original size
# Update keypoints
x = keypoints[:, 0]
y = keypoints[:, 1]
# Compute the tile indices where the keypoints are located
tile_rows = (y / tile_height).long()
tile_cols = (x / tile_width).long()
tile_indices = tile_rows * num_cols + tile_cols # Shape: (num_k,)
# Create inverse mapping from old tile indices to new tile positions
idx_unshuffle = torch.argsort(idx_shuffle) # idx_unshuffle[old_index] = new_index
# Get new tile indices for each keypoint
new_tile_indices = idx_unshuffle[tile_indices]
new_tile_rows = new_tile_indices // num_cols
new_tile_cols = new_tile_indices % num_cols
# Compute offsets within the tile
offset_x = x % tile_width
offset_y = y % tile_height
# Compute new keypoints coordinates
new_x = new_tile_cols * tile_width + offset_x
new_y = new_tile_rows * tile_height + offset_y
# Ensure keypoints are within image boundaries
new_x = new_x.clamp(0, W - 1)
new_y = new_y.clamp(0, H - 1)
new_keypoints = torch.stack([new_x, new_y], dim=1)
return shuffled_image, new_keypoints
def generate_point_map(size, coords0, coords1):
h, w = size[1], size[2]
mask0 = np.zeros((h, w), dtype=np.uint8)
mask1 = np.zeros((h, w), dtype=np.uint8)
for i, (coord0, coord1) in enumerate(zip(coords0, coords1)):
x0, y0 = coord0
x1, y1 = coord1
x0, y0 = int(round(x0)), int(round(y0))
x1, y1 = int(round(x1)), int(round(y1))
if 0 <= x0 < w and 0 <= y0 < h:
mask0[y0, x0] = i + 1
if 0 <= x1 < w and 0 <= y1 < h:
mask1[y1, x1] = i + 1
return mask0, mask1
def select_multiple_points(points0, points1, num_points):
N = len(points0)
num_points = min(num_points, N)
indices = np.random.choice(N, size=num_points, replace=False)
selected_points0 = points0[indices]
selected_points1 = points1[indices]
return selected_points0, selected_points1
def generate_point_map_frames(size, coords0, coords1,visibility):
h, w = size[1], size[2]
mask0 = np.zeros((h, w), dtype=np.uint8)
num_frames = coords1.shape[0]
mask1 = np.zeros((num_frames, h, w), dtype=np.uint8)
for i, coord0 in enumerate(coords0):
x0, y0 = coord0
x0, y0 = int(round(x0)), int(round(y0))
if 0 <= x0 < w and 0 <= y0 < h:
mask0[y0, x0] = i + 1
for frame_idx in range(num_frames):
coords_frame = coords1[frame_idx]
for i, coord1 in enumerate(coords_frame):
x1, y1 = coord1
x1, y1 = int(round(x1)), int(round(y1))
if 0 <= x1 < w and 0 <= y1 < h and visibility[frame_idx,i]==True:
mask1[frame_idx, y1, x1] = i + 1
return mask0, mask1
import numpy as np
def extract_patches(image, coords, patch_size):
N = coords.shape[0]
channels, H, W = image.shape
patches = np.zeros((N, channels, patch_size, patch_size), dtype=image.dtype)
half_size = patch_size // 2
for i in range(N):
x0, y0 = coords[i]
x0 = int(round(x0))
y0 = int(round(y0))
# Define the patch region in the image
x_start_img = x0 - half_size
x_end_img = x0 + half_size + 1
y_start_img = y0 - half_size
y_end_img = y0 + half_size + 1
# Define the region in the patch to fill
x_start_patch = 0
y_start_patch = 0
x_end_patch = patch_size
y_end_patch = patch_size
# Adjust for boundaries
if x_start_img < 0:
x_start_patch = -x_start_img
x_start_img = 0
if y_start_img < 0:
y_start_patch = -y_start_img
y_start_img = 0
if x_end_img > W:
x_end_patch -= (x_end_img - W)
x_end_img = W
if y_end_img > H:
y_end_patch -= (y_end_img - H)
y_end_img = H
# Calculate the actual sizes
patch_height = y_end_patch - y_start_patch
patch_width = x_end_patch - x_start_patch
img_height = y_end_img - y_start_img
img_width = x_end_img - x_start_img
# Ensure the sizes match
if patch_height != img_height or patch_width != img_width:
min_height = min(patch_height, img_height)
min_width = min(patch_width, img_width)
y_end_patch = y_start_patch + min_height
y_end_img = y_start_img + min_height
x_end_patch = x_start_patch + min_width
x_end_img = x_start_img + min_width
# Assign the image patch to the patches array
patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch] = \
image[:, y_start_img:y_end_img, x_start_img:x_end_img]
return patches
def generate_point_feature_map_frames_naive(image, size, coords0, coords1, visibility, patch_size):
channels, H, W = size
num_frames = coords1.shape[0]
N = coords0.shape[0]
# Extract patches from the reference image at coords0
patches = extract_patches(image, coords0, patch_size)
half_size = patch_size // 2
# Initialize the feature maps
feature_maps = np.zeros((num_frames, channels, H, W), dtype=image.dtype)
for frame_idx in range(num_frames):
feature_map = np.zeros((channels, H, W), dtype=image.dtype)
coords_frame = coords1[frame_idx]
for i in range(N):
if visibility[frame_idx, i]:
x1, y1 = coords_frame[i]
x1 = int(round(x1))
y1 = int(round(y1))
# Define the patch region in the feature map
x_start_map = x1 - half_size
x_end_map = x1 + half_size + 1
y_start_map = y1 - half_size
y_end_map = y1 + half_size + 1
# Define the region in the patch to use
x_start_patch = 0
y_start_patch = 0
x_end_patch = patch_size
y_end_patch = patch_size
# Adjust for boundaries
if x_start_map < 0:
x_start_patch = -x_start_map
x_start_map = 0
if y_start_map < 0:
y_start_patch = -y_start_map
y_start_map = 0
if x_end_map > W:
x_end_patch -= (x_end_map - W)
x_end_map = W
if y_end_map > H:
y_end_patch -= (y_end_map - H)
y_end_map = H
# Calculate the actual sizes
patch_height = y_end_patch - y_start_patch
patch_width = x_end_patch - x_start_patch
map_height = y_end_map - y_start_map
map_width = x_end_map - x_start_map
# Ensure the sizes match
if patch_height != map_height or patch_width != map_width:
min_height = min(patch_height, map_height)
min_width = min(patch_width, map_width)
y_end_patch = y_start_patch + min_height
y_end_map = y_start_map + min_height
x_end_patch = x_start_patch + min_width
x_end_map = x_start_map + min_width
# Place the patch into the feature map
feature_map[:, y_start_map:y_end_map, x_start_map:x_end_map] = \
patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch]
feature_maps[frame_idx] = feature_map
return feature_maps
import os
from PIL import Image
import numpy as np
from moviepy.editor import ImageSequenceClip
def export_gif_side_by_side_complete(ref_frame, sketches, frames, output_gif_path, supp_dir,fps):
"""
Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches.
Args:
- ref_frame (PIL.Image or np.ndarray): The reference image.
- sketches (list): List of sketch images (as numpy arrays or PIL Image objects).
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- output_gif_path (str): Path to save the output GIF.
- fps (int): Frames per second for the GIF and MP4.
"""
# Ensure the output directory exists
output_dir = os.path.dirname(output_gif_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the base name of the output file (without extension)
base_name = os.path.splitext(os.path.basename(output_gif_path))[0]
# Create subdirectories for sketches and frames
sketch_dir = os.path.join(supp_dir,"sketches")
frame_dir = os.path.join(supp_dir,"frames")
os.makedirs(sketch_dir, exist_ok=True)
os.makedirs(frame_dir, exist_ok=True)
# Convert numpy arrays to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches]
ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame
# Get dimensions of images
width, height = pil_frames[0].size
# Resize images
resized_frames = [frame.resize((width, height)) for frame in pil_frames]
resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches]
ref_frame = ref_frame.resize((width, height))
# Save each sketch frame
for i, sketch in enumerate(resized_sketches):
sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png")
sketch.save(sketch_filename)
# Save each frame
for i, frame in enumerate(resized_frames):
frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png")
frame.save(frame_filename)
# Save reference frame
ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png")
ref_frame.save(ref_filename)
# Create a new image for each frame with the three columns
column_frames = []
for i, frame in enumerate(resized_frames):
# Create an empty image with the total width for all three columns
new_width = ref_frame.width + resized_sketches[i].width + frame.width
combined_frame = Image.new('RGB', (new_width, height))
# Paste the reference image, sketch, and frame into the new image
combined_frame.paste(ref_frame, (0, 0))
combined_frame.paste(resized_sketches[i], (ref_frame.width, 0))
combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0))
column_frames.append(combined_frame)
# Calculate frame duration in milliseconds based on fps
frame_duration = int(1000 / fps)
# Save the GIF with columns
column_frames[0].save(output_gif_path,
format='GIF',
append_images=column_frames[1:],
save_all=True,
duration=frame_duration,
loop=0)
# Save the MP4 video with the same content
output_mp4_path = os.path.join(supp_dir , 'result.mp4')
# Convert PIL Images to numpy arrays for moviepy
video_frames = [np.array(frame) for frame in column_frames]
clip = ImageSequenceClip(video_frames, fps=fps)
clip.write_videofile(output_mp4_path, codec='libx264')
def export_gif_with_ref_complete(start_image, frames, end_image, reference_image, output_gif_path, supp_dir, fps):
"""
Export a list of frames into a GIF with columns, save individual images and frames,
and create an MP4 video, following the storage method of 'export_gif_side_by_side_complete'.
Args:
- start_image (PIL.Image or np.ndarray): The starting image.
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- end_image (PIL.Image or np.ndarray): The ending image.
- reference_image (PIL.Image or np.ndarray): The reference image.
- output_gif_path (str): Path to save the output GIF.
- supp_dir (str): Directory to save supplementary files.
- fps (int): Frames per second for the GIF and MP4.
"""
# Ensure the output directory exists
output_dir = os.path.dirname(output_gif_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the base name of the output file (without extension)
base_name = os.path.splitext(os.path.basename(output_gif_path))[0]
# Create subdirectories for images and frames
start_end_dir = os.path.join(supp_dir, "start_end_images")
frame_dir = os.path.join(supp_dir, "frames")
reference_dir = os.path.join(supp_dir, "reference")
os.makedirs(start_end_dir, exist_ok=True)
os.makedirs(frame_dir, exist_ok=True)
os.makedirs(reference_dir, exist_ok=True)
# Convert numpy arrays to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
start_image = Image.fromarray(start_image) if isinstance(start_image, np.ndarray) else start_image
end_image = Image.fromarray(end_image) if isinstance(end_image, np.ndarray) else end_image
reference_image = Image.fromarray(reference_image) if isinstance(reference_image, np.ndarray) else reference_image
# Get dimensions of images
width, height = start_image.size
# Resize images to match the height
reference_image = reference_image.resize((reference_image.width, height))
resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames]
# Save start_image, end_image, and reference_image
start_image_filename = os.path.join(start_end_dir, f"{base_name}_start.png")
start_image.save(start_image_filename)
end_image_filename = os.path.join(start_end_dir, f"{base_name}_end.png")
end_image.save(end_image_filename)
reference_image_filename = os.path.join(reference_dir, f"{base_name}_reference.png")
reference_image.save(reference_image_filename)
# Save each frame
for i, frame in enumerate(resized_frames):
frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png")
frame.save(frame_filename)
# Create a new image for each frame with the columns
column_frames = []
for i, frame in enumerate(resized_frames):
# Calculate the total width for all columns
new_width = start_image.width + reference_image.width + end_image.width + frame.width
combined_frame = Image.new('RGB', (new_width, height))
# Paste the images into the combined frame
combined_frame.paste(start_image, (0, 0))
combined_frame.paste(reference_image, (start_image.width, 0))
combined_frame.paste(end_image, (start_image.width + reference_image.width, 0))
combined_frame.paste(frame, (start_image.width + reference_image.width + end_image.width, 0))
column_frames.append(combined_frame)
# Calculate frame duration in milliseconds based on fps
frame_duration = int(1000 / fps)
# Save the GIF with columns
column_frames[0].save(output_gif_path,
format='GIF',
append_images=column_frames[1:],
save_all=True,
duration=frame_duration,
loop=0)
# Save the MP4 video with the same content
output_mp4_path = os.path.join(supp_dir, 'result.mp4')
# Convert PIL Images to numpy arrays for moviepy
video_frames = [np.array(frame) for frame in column_frames]
clip = ImageSequenceClip(video_frames, fps=fps)
clip.write_videofile(output_mp4_path, codec='libx264')
def export_gif_side_by_side_complete_ablation(ref_frame, sketches, frames, output_gif_path, supp_dir,fps):
"""
Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches.
Args:
- ref_frame (PIL.Image or np.ndarray): The reference image.
- sketches (list): List of sketch images (as numpy arrays or PIL Image objects).
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- output_gif_path (str): Path to save the output GIF.
- fps (int): Frames per second for the GIF and MP4.
"""
# Ensure the output directory exists
output_dir = os.path.dirname(output_gif_path)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the base name of the output file (without extension)
base_name = os.path.splitext(os.path.basename(output_gif_path))[0]
# Create subdirectories for sketches and frames
sketch_dir = os.path.join(supp_dir,"sketches")
frame_dir = os.path.join(supp_dir,"frames")
os.makedirs(sketch_dir, exist_ok=True)
os.makedirs(frame_dir, exist_ok=True)
# Convert numpy arrays to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches]
ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame
# Get dimensions of images
width, height = pil_frames[0].size
# Resize images
resized_frames = [frame.resize((width, height)) for frame in pil_frames]
resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches]
ref_frame = ref_frame.resize((width, height))
# Save each sketch frame
for i, sketch in enumerate(resized_sketches):
sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png")
sketch.save(sketch_filename)
# Save each frame
for i, frame in enumerate(resized_frames):
frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png")
frame.save(frame_filename)
# Save reference frame
ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png")
ref_frame.save(ref_filename)
# Create a new image for each frame with the three columns
column_frames = []
rgb_frames = []
for i, frame in enumerate(resized_frames):
# Create an empty image with the total width for all three columns
new_width = ref_frame.width + resized_sketches[i].width + frame.width
combined_frame = Image.new('RGB', (new_width, height))
# Paste the reference image, sketch, and frame into the new image
combined_frame.paste(ref_frame, (0, 0))
combined_frame.paste(resized_sketches[i], (ref_frame.width, 0))
combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0))
column_frames.append(combined_frame)
rgb_frames.append(frame)
# Calculate frame duration in milliseconds based on fps
frame_duration = int(1000 / fps)
# Save the GIF with columns
column_frames[0].save(output_gif_path,
format='GIF',
append_images=column_frames[1:],
save_all=True,
duration=frame_duration,
loop=0)
# Save the MP4 video with the same content
output_mp4_path = supp_dir+'.mp4'
# Convert PIL Images to numpy arrays for moviepy
video_frames = [np.array(frame) for frame in column_frames]
rgb_frames = [np.array(frame) for frame in rgb_frames]
clip = ImageSequenceClip(rgb_frames, fps=fps)
clip.write_videofile(output_mp4_path, codec='libx264')