74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
import cv2
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import numpy as np
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# Build Gaussian image pyramid
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def build_gaussian_pyramid(img, levels):
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float_img = np.ndarray(shape=img.shape, dtype="float")
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float_img[:] = img
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pyramid = [float_img]
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for i in range(levels-1):
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float_img = cv2.pyrDown(float_img)
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pyramid.append(float_img)
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return pyramid
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# Build Laplacian image pyramid from Gaussian pyramid
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def build_laplacian_pyramid(img, levels):
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gaussian_pyramid = build_gaussian_pyramid(img, levels)
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laplacian_pyramid = []
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for i in range(levels-1):
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upsampled = cv2.pyrUp(gaussian_pyramid[i+1])
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(height, width, depth) = upsampled.shape
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gaussian_pyramid[i] = cv2.resize(gaussian_pyramid[i], (height, width))
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diff = cv2.subtract(gaussian_pyramid[i],upsampled)
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laplacian_pyramid.append(diff)
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laplacian_pyramid.append(gaussian_pyramid[-1])
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return laplacian_pyramid
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# Build video pyramid by building Laplacian pyramid for each frame
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def build_video_pyramid(frames):
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lap_video = []
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for i, frame in enumerate(frames):
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pyramid = build_laplacian_pyramid(frame, 3)
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for j in range(3):
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if i == 0:
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lap_video.append(np.zeros((len(frames), pyramid[j].shape[0], pyramid[j].shape[1], 3)))
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lap_video[j][i] = pyramid[j]
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return lap_video
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# Collapse video pyramid by collapsing each frame's Laplacian pyramid
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def collapse_laplacian_video_pyramid(video, frame_ct):
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collapsed_video = []
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for i in range(frame_ct):
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prev_frame = video[-1][i]
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for level in range(len(video) - 1, 0, -1):
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pyr_up_frame = cv2.pyrUp(prev_frame)
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(height, width, depth) = pyr_up_frame.shape
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prev_level_frame = video[level - 1][i]
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prev_level_frame = cv2.resize(prev_level_frame, (height, width))
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prev_frame = pyr_up_frame + prev_level_frame
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# Normalize pixel values
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min_val = min(0.0, prev_frame.min())
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prev_frame = prev_frame + min_val
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max_val = max(1.0, prev_frame.max())
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prev_frame = prev_frame / max_val
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prev_frame = prev_frame * 255
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prev_frame = cv2.convertScaleAbs(prev_frame)
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collapsed_video.append(prev_frame)
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return collapsed_video
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