|
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273 |
- import cv2
- import numpy as np
-
-
- # Build Gaussian image pyramid
- def build_gaussian_pyramid(img, levels):
- float_img = np.ndarray(shape=img.shape, dtype="float")
- float_img[:] = img
- pyramid = [float_img]
-
- for i in range(levels-1):
- float_img = cv2.pyrDown(float_img)
- pyramid.append(float_img)
-
- return pyramid
-
-
- # Build Laplacian image pyramid from Gaussian pyramid
- def build_laplacian_pyramid(img, levels):
- gaussian_pyramid = build_gaussian_pyramid(img, levels)
- laplacian_pyramid = []
-
- for i in range(levels-1):
- upsampled = cv2.pyrUp(gaussian_pyramid[i+1])
- (height, width, depth) = upsampled.shape
- gaussian_pyramid[i] = cv2.resize(gaussian_pyramid[i], (height, width))
- diff = cv2.subtract(gaussian_pyramid[i],upsampled)
- laplacian_pyramid.append(diff)
-
- laplacian_pyramid.append(gaussian_pyramid[-1])
-
- return laplacian_pyramid
-
-
- # Build video pyramid by building Laplacian pyramid for each frame
- def build_video_pyramid(frames):
- lap_video = []
-
- for i, frame in enumerate(frames):
- pyramid = build_laplacian_pyramid(frame, 3)
- for j in range(3):
- if i == 0:
- lap_video.append(np.zeros((len(frames), pyramid[j].shape[0], pyramid[j].shape[1], 3)))
- lap_video[j][i] = pyramid[j]
-
- return lap_video
-
-
- # Collapse video pyramid by collapsing each frame's Laplacian pyramid
- def collapse_laplacian_video_pyramid(video, frame_ct):
- collapsed_video = []
-
- for i in range(frame_ct):
- prev_frame = video[-1][i]
-
- for level in range(len(video) - 1, 0, -1):
- pyr_up_frame = cv2.pyrUp(prev_frame)
- (height, width, depth) = pyr_up_frame.shape
- prev_level_frame = video[level - 1][i]
- prev_level_frame = cv2.resize(prev_level_frame, (height, width))
- prev_frame = pyr_up_frame + prev_level_frame
-
- # Normalize pixel values
- min_val = min(0.0, prev_frame.min())
- prev_frame = prev_frame + min_val
- max_val = max(1.0, prev_frame.max())
- prev_frame = prev_frame / max_val
- prev_frame = prev_frame * 255
-
- prev_frame = cv2.convertScaleAbs(prev_frame)
- collapsed_video.append(prev_frame)
-
- return collapsed_video
|
