CU40 with Numpy Slicing (efficient for video on Raspberry Pi)

see3cam_good_np_slicing.py

# TESTING HARDWARE
# see3CAM_CU40
# raspberry Pi 3 bf8
# Raspbian Stretch 13-03-2018
# Python 3.5.3
# OpenCV 3.4.1

# load required libraries
import math
import numpy as np
import cv2

# open up camera on port 0
cam = cv2.VideoCapture(0)

# CU40 has the following possible resolution settings
# 672 x 380
# 1280 x 720
# 1920 x 1080
# 2688 x 1520

rows = 1520
cols = 2688

#set dimensions
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, rows)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, cols)


# turn off automatic RGB conversion
# this sets capture to 16bit
cam.set(cv2.CAP_PROP_CONVERT_RGB, False) 
# don't need to set the FOURCC, this is handled in OpenCV cap_libv4l.cpp
# cam.set(cv2.CAP_PROP_FOURCC('Y', '1', '6', ''))

# get image from sensor module
ret_val, frame = cam.read()

# release camera
cam = cam.release()

# uncomment this line to write out raw 16 bit image captured
# recall that the image is 16 bit, but the values are 10-bit (0-1023)
#cv2.imwrite('raw.pgm', im)

# CU40 camera
# convert from 10 bit (1024 levels) to 8 bit (256 levels) 256/104 = 0.25
bf8 = np.array(frame//4, dtype = np.uint8)

# uncomment this line to write out scaled (10 bit to 8 bit) raw image
#cv2.imwrite('rawscaled.pgm', bf8)

# Typical Bayer filter is RGGB
# B is 0,0; R is 1,1
# B G
# G R
# econ sensors are RGIRB
# B IR  -> B G
# G R   -> G R

# create a copy of the bayer array to replace IR with nearest G
bRGGB = np.copy(bf8)

# IR array is 1/4 of the pixels
# create 8-bit array of 0s to fill
IR = np.zeros([rows//2, cols//2], np.uint8)

# copy out IR pixels
IR = bf8[1::2, 0::2]
# copy over IR pixels with nearest G pixel
bf8[1::2, 0::2] = bf8[0::2, 1::2]

# convert Bayer RGGB into BGR image [rows, cols, dim = 3]
# cv2.COLOR_BayerRG2BGR bayer demosaicing
# cv2.COLOR_BayerRG2BGR_EA edge aware demosaicing
BGRim = cv2.cvtColor(bRGGB, cv2.COLOR_BayerRG2BGR_EA)


# write out RGB image
cv2.imwrite('RGB.png', BGRim)
# write out IR image
cv2.imwrite('IR.png', IR)

see3cam_good_splitchannels_np_slicing.py

# TESTING HARDWARE
# see3CAM_CU40
# raspberry Pi 3 bf8
# Raspbian Stretch 13-03-2018
# Python 3.5.3
# OpenCV 3.4.1

# load required libraries
import math
import numpy as np
import cv2

# open up camera on port 0
cam = cv2.VideoCapture(0)

# CU40 has the following possible resolution settings
# 672 x 380
# 1280 x 720
# 1920 x 1080
# 2688 x 1520

rows = 1520
cols = 2688

#set dimensions
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, rows)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, cols)


# turn off automatic RGB conversion
# this sets capture to 16bit
cam.set(cv2.CAP_PROP_CONVERT_RGB, False) 
# don't need to set the FOURCC, this is handled in OpenCV cap_libv4l.cpp
# cam.set(cv2.CAP_PROP_FOURCC('Y', '1', '6', ''))

# get image from sensor module and trash return read value
_, frame = cam.read()

# release camera
cam = cam.release()

# uncomment this line to write out raw 16 bit image captured
# recall that the image is 16 bit, but the values are 10-bit (0-1023)
#cv2.imwrite('raw.pgm', im)

# CU40 camera
# convert from 10 bit (1024 levels) to 8 bit (256 levels) 256/104 = 0.25
bf8 = np.array(frame//4, dtype = np.uint8)

# uncomment this line to write out scaled (10 bit to 8 bit) raw image
#cv2.imwrite('rawscaled.pgm', bf8)

# Typical Bayer filter is RGGB
# B is 0,0; R is 1,1
# B G
# G R
# econ sensors are RGIRB
# B IR  -> B G
# G R   -> G R

# create a copy of the bayer array to replace IR with nearest G
#bRGGB = np.copy(bf8)

# IR array is 1/4 of the pixels
# create 8-bit array of 0s to fill
IR = np.zeros([rows//2, cols//2], np.uint8)

BGR = np.zeros([rows//2, cols//2, 3], np.uint8)

#set R
BGR[:, :, 2] = bf8[1::2, 1::2]
#set G
BGR[:, :, 1] = bf8[0::2, 1::2]
#set B
BGR[:, :, 0] = bf8[0::2, 0::2]

#set IR data into new array
IR = bf8[1::2, 0::2]

# write out RGB image
cv2.imwrite('RGB.png', BGR)
# write out IR image
cv2.imwrite('IR.png', IR)

see3cam_stream_np_slicing.py

import time
#import subprocess
import math
import numpy as np
import cv2

# subprocess.call("v4l2-ctl -d /dev/video0 brightness=0", shell=True)

cv2.namedWindow("bf8", cv2.WINDOW_AUTOSIZE)
cv2.namedWindow("IR", cv2.WINDOW_AUTOSIZE)

vs = cv2.VideoCapture(0)

# CU40 has the following possible resolution settings
# 672 x 380
# 1280 x 720
# 1920 x 1080
# 2688 x 1520

rows = 380
cols = 672
# create array for IR image to fill    
IR = np.zeros([rows//2, cols//2], np.uint8)

vs.set(cv2.CAP_PROP_FRAME_WIDTH, cols)
vs.set(cv2.CAP_PROP_FRAME_HEIGHT, rows)

vs.set(cv2.CAP_PROP_CONVERT_RGB, False) # turn off RGB conversion

while True:
    #grab capture value (into null) and frame 
    _, frame = vs.read()
    # CU40 camera
    # convert from 10 bit (1024 levels) to 8 bit (256 levels) 256/104 = 0.25
    bf8 = np.array(frame//4, dtype = np.uint8)
    
    # copy out IR pixels
    IR = bf8[1::2, 0::2]
    # copy over IR pixels with nearest G pixel
    bf8[1::2, 0::2] = bf8[0::2, 1::2]
	
    #convert Bayer to RGB
    bf8 = cv2.cvtColor(bf8, cv2.COLOR_BayerRG2BGR)
    # show results
    cv2.imshow("bf8", bf8)
    cv2.imshow("IR", IR)
    
    # detect waitkey of q to quit
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

cv2.destroyAllWindows()
vs.release()