File size: 2,240 Bytes
d3092e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import numpy as np
import math
def calculate_padding(image, patch_size=256):
"""
Calculates required padding amounts
ARGS:
image: Image to be padded (height, width, channels)
patch_size: Stride of the patches
RETURNS:
padding: Padding amounts (top, bottom, left, right)
"""
height, width = image.shape[-3:-1]
# Calculate Desired shape
xx = (width // patch_size +1) * patch_size
yy = (height // patch_size +1) * patch_size
# Calculate padding amounts
x_pad = xx - width
y_pad = yy - height
x0 = x_pad // 2
x1 = x_pad - x0
y0 = y_pad // 2
y1 = y_pad - y0
return (y0, y1, x0, x1)
def apply_padding(image, patch_size=256):
"""
Adds padding to the image to make patches of equal size that covers the entire image
ARGS:
image: Image to be padded
patch_size: Stride of the patches
RETURNS:
padded_image: Padded image
"""
width, height = image.shape[:2]
pad_y0, pad_y1, pad_x0, pad_x1 = calculate_padding(image, patch_size)
return np.pad(image, ((pad_y0, pad_y1), (pad_x0, pad_x1), (0, 0)), mode='constant')
def predict(model, img, threshold=0.5, patch_size=256):
"""
model: a keras model
img: a np array representing the image (expects float dtype and values between 0 and 1)
"""
padded = apply_padding(img, patch_size)
number_of_rows, number_of_columns = math.ceil(img.shape[0] /patch_size), math.ceil(img.shape[1] /patch_size)
# fig, axes = plt.subplots(number_of_rows, number_of_columns, figsize=(20, 20))
prediction = np.zeros((number_of_rows * patch_size, number_of_columns * patch_size, 1), dtype="float32")
for i in range(0, number_of_rows):
for j in range(0, number_of_columns):
# Convert to float
slc = padded[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size] / 256.0
pred = model.predict(np.expand_dims(slc, axis = 0), verbose = 0)
# axes[i][j].imshow(pred[0])
prediction[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size] = pred[0]
# Threshold the results
prediction = (prediction > threshold).astype(np.uint8)
# Reverse padding
y0, y1, x0, x1 = calculate_padding(img, patch_size)
preds = prediction[y0:-y1, x0:-x1]
return preds
|