Suppose I want to represent binary data as a black and white image, with only sixteen distinct levels for the gray values for each pixel so that each two adjacent pixels (lengthwise) represent a single byte. How can I do this? If, for example, I use the following:
import numpy as np
path = r'mybinaryfile.bin'
bin_data = np.fromfile(path, dtype='uint8')
scalar = 20
width = int(1800/scalar)
height = int(1000/scalar)
for jj in range(50):
wid = int(width*height)
img = bin_data[int(jj*wid):int((jj 1)*wid)].reshape(height,width)
final_img = cv2.resize(img, (scalar*width, scalar*height), interpolation = cv2.INTER_NEAREST)
fn = f'tmp/output_{jj}.png'
cv2.imwrite(fn, final_img)
I can create a sequence of PNG files that represent the binary file, with each 20 by 20 block of pixels representing a single byte. However, this creates too many unique values for the grays (256), so I need to reduce it to fewer (16). How can I "split" each pixel into two pixels with 16 distinct gray levels (4 bpp, rather than 8) instead?
Using 4 bpp rather than 8 bpp should double the number of image files since I'm keeping the resolution the same but doubling the number of pixels I use to represent a byte (2 pixels per byte rather than 1).
CodePudding user response:
I have understood that you want to take an 8-bit number and split the upper four bits and the lower four bits.
This can be done with a couple of bitwise operations.
def split_octet(data):
"""
For each 8-bit number in array, split them into two 4-bit numbers"""
split_data = []
for octet in data:
upper = octet >> 4
lower = octet & 0x0f
print(f"8bit:{octet:02x} upper:{upper:01x} and lower:{lower:01x}")
split_data.extend([upper, lower])
return split_data
For the gray scale image to be created the data needs to be converted to a value in the range 0 to 255. However you want to keep only 16 discrete values. This can be done by normalising the 4-bit values in the range of 0 to 1. The multiple the value by 255 to get back to uint8 values.
def create_square_grayscale(data, data_shape):
# Normalize data from 0 to 1
normalized = np.array(data, np.float64) / 0xf
# fold data to image shape
pixel_array = normalized.reshape(data_shape)
# change 16 possible values over 0 to 255 range
return np.array(pixel_array * 0xff, np.uint8)
My full testcase was:
from secrets import token_bytes
import cv2
import numpy as np
pixel_size = 20
final_image_size = (120, 120)
def gen_data(data_size):
# Generate some random data
return token_bytes(data_size)
def split_octet(data):
"""
For each 8-bit number in array, split them into two 4-bit numbers"""
split_data = []
for octet in data:
upper = octet >> 4
lower = octet & 0x0f
print(f"8bit:{octet:02x} upper:{upper:01x} and lower:{lower:01x}")
split_data.extend([upper, lower])
return split_data
def create_square_grayscale(data, data_shape):
# Normalize data from 0 to 1
normalized = np.array(data, np.float64) / 0xf
# fold data to image shape
pixel_array = normalized.reshape(data_shape)
# change 16 possible values over 0 to 255 range
return np.array(pixel_array * 0xff, np.uint8)
def main():
side1, side2 = (int(final_image_size[0]/pixel_size),
int(final_image_size[1]/pixel_size))
rnd_data = gen_data(int((side1 * side2)/2))
split_data = split_octet(rnd_data)
img = create_square_grayscale(split_data, (side1, side2))
print("image data:\n", img)
new_res = cv2.resize(img, None, fx=pixel_size, fy=pixel_size,
interpolation=cv2.INTER_AREA)
cv2.imwrite("/tmp/rnd.png", new_res)
if __name__ == '__main__':
main()
Which gave a transcript of:
8bit:34 upper:3 and lower:4
8bit:d4 upper:d and lower:4
8bit:bd upper:b and lower:d
8bit:c3 upper:c and lower:3
8bit:61 upper:6 and lower:1
8bit:9e upper:9 and lower:e
8bit:5f upper:5 and lower:f
8bit:1b upper:1 and lower:b
8bit:a5 upper:a and lower:5
8bit:31 upper:3 and lower:1
8bit:22 upper:2 and lower:2
8bit:8a upper:8 and lower:a
8bit:1e upper:1 and lower:e
8bit:84 upper:8 and lower:4
8bit:3a upper:3 and lower:a
8bit:c0 upper:c and lower:0
8bit:3c upper:3 and lower:c
8bit:09 upper:0 and lower:9
image data:
[[ 51 68 221 68 187 221]
[204 51 102 17 153 238]
[ 85 255 17 187 170 85]
[ 51 17 34 34 136 170]
[ 17 238 136 68 51 170]
[204 0 51 204 0 153]]
And generated the following image:
The original data has 18 bytes and there are 36 blocks/"20x20_pixels"
And if I change the dimensions to 1800, 1000 that you have in the question I get: