I am struggling a bit with a problem, I was wondering if someone with more experience might notice what am I doing wrong:
I have a binary file of 6,266,880 bytes that contain an image saved with an unknown Bayer pattern .
About the image I know that it's format is 2176x1920 pixels, and that it has a bit_per_pixel
= 12.
I would like to discover which one is the Bayer format used to save the image .
I thought to convert it with cv2.cvtColor(src, cv2.COLOR_BayerGR2BGR)
using all the conversion options provided by the cv2 library for Bayer input, which are:
cv::COLOR_BayerBG2BGR
cv::COLOR_BayerGB2BGR
cv::COLOR_BayerRG2BGR
cv::COLOR_BayerGR2BGR
Until I would have found the one that provide as output a "clean" image.
However, I am always getting something dirty like this:
Here's the code that I am using:
import numpy as np
import matplotlib.pyplot as plt
import cv2
pixels = np.fromfile("0000.raw", dtype = 'uint8')
""" CONVERT THE BYTE STREAM, EVERY PIXEL HAS 12 BIT, SO BYTE HAS TO BE SPLITTED AND PUTTED IN A UINT16 VARIABLE"""
data = pixels
data1 = data.astype(np.uint16)
data1[::3] = data1[::3]*256 + data1[1::3] // 16
data1[1::3] = (data[1::3] & 0x0f)*16 + data[2::3]
result = np.ravel(data1.reshape(-1,3)[:,:2])
img = result.reshape(2176, 1920)
convertedImage = cv2.demosaicing(img_scaled, cv2.COLOR_BayerGR2BGR)
cv2.imshow("tmp", convertedImage)
cv2.waitKey(0)
Also, Here there are 10 samples of the same image saved as raw file, and for each of them a json with their properties
Any idea on what else to try to convert it? Or is there some other approach to find the Bayer format?
The 12 bits are packed: Every 3 bytes applies packed 2 (12 bits) pixels.
I managed to unpack the pixels by trial and error.
Here is the code:
import numpy as np
import cv2
cols, rows = 1920, 2176
pixels = np.fromfile("0000.raw", np.uint8)
""" CONVERT THE BYTE STREAM, EVERY PIXEL HAS 12 BIT, SO BYTE HAS TO BE SPLITTED AND PUTTED IN A UINT16 VARIABLE"""
data = pixels
data1 = data.astype(np.uint16)
result = np.zeros(data.size*2//3, np.uint16)
# 12 bits packing: ######## ######## ########
# | 8bits| | 4 | 4 | 8 |
# | lsb | |msb|lsb | msb |
# <-----------><----------->
# 12 bits 12 bits
result[0::2] = ((data1[1::3] & 15) << 8) | data1[0::3]
result[1::2] = (data1[1::3] >> 4) | (data1[2::3] << 4)
bayer_im = np.reshape(result, (rows, cols))
bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerBG2BGR)
cv2.imshow('bgr', bgr*16)
# "White balance":
bgr[:, :, 0] = np.minimum(bgr[:, :, 0].astype(np.float32)*1.8, 4095).astype(np.uint16)
bgr[:, :, 2] = np.minimum(bgr[:, :, 2].astype(np.float32)*1.67, 4095).astype(np.uint16)
cv2.imshow('bayer_im', bayer_im*16)
cv2.imshow('bgr WB', bgr*16)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.COLOR_BayerBG2BGR
gives the best result.
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