DaSE-Computer-Vision-2021
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from builtins import range
from past.builtins import xrange
from math import sqrt, ceil
import numpy as np
def visualize_grid(Xs, ubound=255.0, padding=1):
"""
Reshape a 4D tensor of image data to a grid for easy visualization.
Inputs:
- Xs: Data of shape (N, H, W, C)
- ubound: Output grid will have values scaled to the range [0, ubound]
- padding: The number of blank pixels between elements of the grid
"""
(N, H, W, C) = Xs.shape
grid_size = int(ceil(sqrt(N)))
grid_height = H * grid_size + padding * (grid_size - 1)
grid_width = W * grid_size + padding * (grid_size - 1)
grid = np.zeros((grid_height, grid_width, C))
next_idx = 0
y0, y1 = 0, H
for y in range(grid_size):
x0, x1 = 0, W
for x in range(grid_size):
if next_idx < N:
img = Xs[next_idx]
low, high = np.min(img), np.max(img)
grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low)
# grid[y0:y1, x0:x1] = Xs[next_idx]
next_idx += 1
x0 += W + padding
x1 += W + padding
y0 += H + padding
y1 += H + padding
# grid_max = np.max(grid)
# grid_min = np.min(grid)
# grid = ubound * (grid - grid_min) / (grid_max - grid_min)
return grid
def vis_grid(Xs):
""" visualize a grid of images """
(N, H, W, C) = Xs.shape
A = int(ceil(sqrt(N)))
G = np.ones((A*H+A, A*W+A, C), Xs.dtype)
G *= np.min(Xs)
n = 0
for y in range(A):
for x in range(A):
if n < N:
G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = Xs[n,:,:,:]
n += 1
# normalize to [0,1]
maxg = G.max()
ming = G.min()
G = (G - ming)/(maxg-ming)
return G
def vis_nn(rows):
""" visualize array of arrays of images """
N = len(rows)
D = len(rows[0])
H,W,C = rows[0][0].shape
Xs = rows[0][0]
G = np.ones((N*H+N, D*W+D, C), Xs.dtype)
for y in range(N):
for x in range(D):
G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = rows[y][x]
# normalize to [0,1]
maxg = G.max()
ming = G.min()
G = (G - ming)/(maxg-ming)
return G