Spaces:
Running
on
L40S
Running
on
L40S
| 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 | |