import imageio
import numpy as np
from PIL import Image, ImageDraw, ImageEnhance
from scipy.ndimage import gaussian_filter1d
def draw_annotations_for_extended_frames(video_batch, start_index_prediction=17):
"""
video_batch List of list of PIL.Image frames
"""
radius = 2.5
offset = 10
for video in video_batch:
assert start_index_prediction < len(video), f"Index {start_index_prediction} is out-of-bound for frames"
for i_idx, image in enumerate(video):
if i_idx < start_index_prediction:
continue
draw = ImageDraw.Draw(image)
draw.ellipse([offset, offset, offset+2*radius, offset+2*radius], fill=(255,0,0))
return video_batch
def draw_annotations_for_initial_frames(video_batch, end_index_prediction=17):
"""
video_batch List of list of PIL.Image frames
"""
radius = 2.5
offset = 10
for video in video_batch:
assert end_index_prediction < len(video), f"Index {end_index_prediction} is out-of-bound for frames"
for i_idx, image in enumerate(video):
if i_idx >= end_index_prediction:
continue
draw = ImageDraw.Draw(image)
draw.ellipse([offset, offset, offset+2*radius, offset+2*radius], fill=(255,0,0))
return video_batch
def images_to_array(images):
return np.array([np.array(img) for img in images])
def array_to_images(array):
return [Image.fromarray(arr) for arr in array]
def save_video_mp4(path, video, fps=12):
imageio.mimwrite(
path,
video,
format="mp4",
fps=fps,
codec="libx264",
output_params=["-pix_fmt", "yuv420p"],
)
def blend_pixels_temporal(video_batch, start_index_prediction=17, sigma=1, support=3):
for video in video_batch:
assert start_index_prediction < len(video) and start_index_prediction > 0, f"Index {start_index_prediction} is out-of-bound for frames"
# blur temporally
video_array = images_to_array(video)
start = max(start_index_prediction - support // 2, 0)
end = min(start_index_prediction + support // 2 + 1, video_array.shape[0])
# only blend in the first frame
video_array[start_index_prediction] = gaussian_filter1d(video_array[start:end],
sigma=sigma,
axis=0,
truncate=support/2)[support//2]
# uncomment to blend in "support" frames, which causes noticeable blurs in some cases
#video_array[start:end] = gaussian_filter1d(video_array[start:end],
# sigma=sigma,
# axis=0,
# truncate=support/2)
blurred_video = array_to_images(video_array)
for i in range(len(video)):
video[i] = blurred_video[i]
return video_batch
def calculate_mean_std(image_array, channel):
channel_data = image_array[:, :, channel]
return channel_data.mean(), channel_data.std()
def adjust_mean(image, target_mean, channel):
channel_data = np.array(image)[:, :, channel]
current_mean = channel_data.mean()
adjusted_data = channel_data + (target_mean - current_mean)
adjusted_data = np.clip(adjusted_data, 0, 255).astype(np.uint8)
image_np = np.array(image)
image_np[:, :, channel] = adjusted_data
return Image.fromarray(image_np)
def adjust_contrast(image, target_contrast, channel):
channel_data = np.array(image)[:, :, channel]
current_mean = channel_data.mean()
current_contrast = channel_data.std()
if current_contrast == 0:
adjusted_data = current_mean * np.ones_like(channel_data)
else:
adjusted_data = (channel_data - current_mean) * (target_contrast / current_contrast) + current_mean
adjusted_data = np.clip(adjusted_data, 0, 255).astype(np.uint8)
image_np = np.array(image)
image_np[:, :, channel] = adjusted_data
return Image.fromarray(image_np)
def calculate_brightness(image):
grayscale = image.convert("L")
histogram = grayscale.histogram()
pixels = sum(histogram)
brightness = scale = len(histogram)
for index in range(scale):
ratio = histogram[index] / pixels
brightness += ratio * (-scale + index)
return 1 if brightness == 255 else brightness / scale
def calculate_contrast(image):
grayscale = image.convert("L")
histogram = grayscale.histogram()
pixels = sum(histogram)
mean = sum(i * w for i, w in enumerate(histogram)) / pixels
contrast = sum((i - mean) ** 2 * w for i, w in enumerate(histogram)) / pixels
return contrast ** 0.5
def adjust_brightness_contrast(image, target_brightness, target_contrast):
current_brightness = calculate_brightness(image)
brightness_enhancer = ImageEnhance.Brightness(image)
image = brightness_enhancer.enhance(target_brightness / current_brightness)
current_contrast = calculate_contrast(image)
contrast_enhancer = ImageEnhance.Contrast(image)
image = contrast_enhancer.enhance(target_contrast / current_contrast)
return image
def adjust_statistics_to_match_reference(video_batch,
start_index_prediction=17,
reference_window_size=3):
assert start_index_prediction > 1, f"Need at least 1 frame before prediction start"
assert start_index_prediction > reference_window_size, f"Reference window size incorrect: {start_index_prediction} <= {reference_window_size}"
for video in video_batch:
window_start = max(start_index_prediction - reference_window_size, 0)
## first adjust the mean and contrast of each color channel
#video_array = images_to_array(video)
#window_frames = video_array[window_start:start_index_prediction]
#for channel in range(3):
# window_mean, window_std = calculate_mean_std(window_frames, channel)
# for ii in range(start_index_prediction, len(video)):
# video[ii] = adjust_mean(video[ii], window_mean, channel)
# video[ii] = adjust_contrast(video[ii], window_std, channel)
# then adjust the overall brightness and contrast
window_brightness = np.mean(
[calculate_brightness(video[jj]) for jj in range(window_start, start_index_prediction)])
window_contrast = np.mean(
[calculate_contrast(video[jj]) for jj in range(window_start, start_index_prediction)])
for ii in range(start_index_prediction, len(video)):
video[ii] = adjust_brightness_contrast(video[ii], window_brightness, window_contrast)
return video_batch