beadpos_xcol = np.empty(MAX_BEADS_PERFRAME, dtype=np.uint16)
beadpos_yrow = np.empty(MAX_BEADS_PERFRAME, dtype=np.uint16)
segmask_image = np.empty(data_size, dtype=np.uint16)
im = np.fromfile("09162009D_00_1000.raw", dtype=np.uint16)
# the file format is a 6MB file on uint16s so
# 1st 2MB is raw image
# 2nd 2MB is flattened image
# 3rd 2MB is binary object mask
raw_image = im[0:1000000]
binary_object_mask = im[2000000:3000000]
print("The size is %d" % binary_object_mask.size)
objects_found_old = np.where(binary_object_mask==1)[0].size
print("Chao found %d objects" % objects_found_old)
num_beads = FO.find_objects(1000, 1000, \
raw_image, \
beadpos_xcol, beadpos_yrow, \
segmask_image)
print("I found %d objects" % num_beads)
binary_object_mask = binary_object_mask*65535
segmask_image = segmask_image*65535
binary_object_mask = binary_object_mask.astype(np.uint8)
segmask_image = segmask_image.astype(np.uint8)
imChao = Image.fromarray(np.reshape(binary_object_mask, (1000, 1000) ))
imNick = Image.fromarray(np.reshape(segmask_image, (1000, 1000) ))
imChao.show(command="display")
imNick.show(command="display")
#imChao.show(command="eog")
#imNick.show(command="eog")
def find():
"""
This function:
1) Takes all images, saves with labels "_before" for all
2) Then finds all the objects of one color "mycolor",
a) takes the images averaging over "frames" images
a) finds all objects
b) prints out a bunch of bead position values found in the image
3) creates an overlay image putting the "mycolor" object centroids on top
of the raw images of the composite of all colors and saves a png
of 'colorsnap-'+'find_overlay_' + my_color +'_before' + ".png
"""
# declare all globals for function
global img_array
global img_array_float
global img_array_flat
global beadpos_xcol
global beadpos_yrow
global segmask_image
global exposures
global gains
global colors
global frames
global num_beads
global my_color
global my_color_ind
global MapFunc
global MaestroF
num_beads = 0
MaestroF.darkfield_off()
"""
DO NOT Home the filter wheel!!! In between sequencing and releasing
This destroys the mapping, since the filter whell cannot reapeatedly home
To the same postion
"""
#MaestroF.filter_home()
snapAllPics(exposures, gains, "_before")
MaestroF.filter_goto(my_color)
time.sleep(1)
for i in range(frames):
PC.py_snapPtr(img_array, \
exposures[my_color_ind], \
gains[my_color_ind] , \
my_color)
img_array_float += img_array.astype(np.float)
# end for
img_array_out = (img_array_float/frames).astype(np.uint16)
img_array_float *= 0 # reset accumulator
#invert for images where beads are lighter than background
IT.flatten_image(1000, 1000, img_array_out, img_array_flat,1)
img_array_flat = 16383 - img_array_flat # invert the 14 bit image
num_beads = FO.find_objects(1000, 1000, \
img_array_flat, \
beadpos_xcol, beadpos_yrow, \
segmask_image)
print("The number of beads found: %d" % num_beads)
MapFunc.convertPicPNG2(16383-img_array_flat,0,1)
MapFunc.convertPicPNG2(segmask_image*65535, 0,2)
print("phase ONE complete!!!!!!!!!!!!!")
print("value at 1st point: %d at point %d, %d" % \
(img_array_out[beadpos_xcol[0]+1000*beadpos_yrow[0]], \
beadpos_xcol[0], beadpos_yrow[0]))
print("value at 2nd point: %d at point %d, %d" % \
(img_array_out[beadpos_xcol[1]+1000*beadpos_yrow[1]], \
beadpos_xcol[1], beadpos_yrow[1]))
print("value at 3rd point: %d at point %d, %d" % \
(img_array_out[beadpos_xcol[2]+1000*beadpos_yrow[2]], \
beadpos_xcol[2], beadpos_yrow[2]))
#PC.cameraClose() # also frees up image buffer memory
stop_release()
"""
imG = Image.open("map_0_1" +".png") # just beads
imB = Image.open("map_0_2" +".png") # the found beads
im = Image.new('RGB', (1000,1000))
pix = im.load()
pixG = imG.load()
pixB = imB.load()
for i in range(1000):
for j in range(1000):
pix[i,j] = (0,pixG[i,j],pixB[i,j]) #px = -238501
# end for
# end for
im.save("color_overlay_orig" +".png", "png")
"""
"""
saves the pretaken raw 14bit image as an 8 bit greyscale PNG
by shifting out 6 bits
"""
shape = (1000, 1000)
img_1D_list = []
for c in colors:
img_file = open('colorsnap-'+c+'_before' +'.raw', 'rb')
# load a 1000000 length array
img_array_1D = np.fromfile(file=img_file, dtype=np.uint16)
img_1D_list.append(img_array_1D.reshape(shape))
img_file.close()
del img_array_1D
# end for
# R = cy5, G = cy3, B = txRed
# yellow = fam ==> RGB:255,255,0
img_total = (img_1D_list[1] + \
img_1D_list[2] + \
img_1D_list[3]).clip(min=None,max=16383).astype(np.uint16)
img_3D = np.dstack([img_total, \
16383*segmask_image.reshape(shape), \
img_1D_list[my_color_ind]])
img_8bit = (img_3D >> 6).astype(np.uint8)
#im = Image.fromarray(img_8bit.reshape(-1,1000*3),'RGB')
im = Image.fromarray(img_8bit,'RGB')
im.save('colorsnap-'+'find_overlay_' + my_color +'_before' + ".png", "png")
# clean house
del img_array_out
del img_total
del img_3D
del img_8bit
del im
del shape
del img_1D_list
segmask_image = np.empty(data_size, dtype=np.uint16)
MaestroF.darkfield_off()
MaestroF.filter_home()
MaestroF.filter_goto(my_color)
time.sleep(1)
MapFunc.init_illum()
MapFunc.light_all()
PC.py_snapPtr(img_array, .35,80,my_color)
#invert for images where beads are lighter than background
img_array = 16383 - img_array # invert the 14 bit image
num_beads = FO.find_objects(1000, 1000, \
img_array, \
beadpos_xcol, beadpos_yrow, \
segmask_image)
print("The number of beads found: %d" % num_beads)
MapFunc.convertPicPNG2(16383-img_array,0,1)
MapFunc.convertPicPNG2(segmask_image*65535, 0,2)
#MapFunc.convertPicPNG(segmask_image*65535+img_array, 0,3)
print("phase ONE complete!!!!!!!!!!!!!")
PC.cameraClose() # also frees up image buffer memory
#MapFunc.illum_bitmap()
MapFunc.illum_vector(beadpos_yrow,beadpos_xcol)
time.sleep(1)
