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from .BaseReport import BaseReport
from utils import imgproc
import skimage.exposure as ske
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
class BrightnessReport(BaseReport):
def __init__(self,imgstack_file,coord_info,fov,z):
super().__init__(imgstack_file,coord_info)
self.fov_name = fov
self.z_name = z
self.imgstack = self.imgstack # (y,x,wvs,irs)
self.contrast_tape = np.zeros((self.imgstack.shape[2],
self.imgstack.shape[3]))
#Helper----------------------------------------------
def calc_HS_metric(self,img):
'''
https://ieeexplore.ieee.org/document/6108900
'''
vals = np.percentile(img.ravel(),[75,25])
max_val = np.max(img)
min_val = np.min(img)
return (vals[0]-vals[1])/(max_val-min_val)
def calc_HF_metric(self,img):
'''
https://ieeexplore.ieee.org/document/6108900
'''
hist,_ = np.histogram(img.flatten(),bins = int(img.max()//2),range=(0,img.max()))
return np.power(np.prod(hist),1/len(hist)) / hist.sum() * len(hist)
def ski_is_low_contrast(self,img,fraction_threshold = 0.25):
return ske.is_low_contrast(img,fraction_threshold=fraction_threshold)
def contrast_test(self,img,threshold=0.25,method='ski'):
if method=='ski':
return self.ski_is_low_contrast(img,threshold)
elif method =='HS':
res = self.calc_HS_metric(img)
return res>threshold
elif method =='HF':
res = self.calc_HF_metric(img)
return res>threshold
#Reports----------------------------------------------
def preview_images(self):
max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1)
val_range = max_wv_val*0.9#self.imgstack.max()*0.90
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
img = self.imgstack[:,:,iwv,iir]
ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def brightness_infov_z(self):
largest = self.imgstack.max()
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
bin_num = int(largest//2)
data = self.imgstack[:,:,iwv,iir]
self.contrast_tape[iwv,iir] = self.calc_HS_metric(data)
flat_data = data.ravel()
ax[iir,iwv].hist(flat_data,bins=bin_num,range=(0,largest),log=True,histtype='step')
ax[iir,iwv].text(0.5, 0.5, f'HS:{self.contrast_tape[iwv,iir]}',
ha="center", va="center",
transform=ax[iir,iwv].transAxes)
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def brightness_on_images(self):
max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1)
val_range = max_wv_val*0.9
largest = self.imgstack.max()
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
img = self.imgstack[:,:,iwv,iir]
bin_num = int(largest//2)
ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
# Add histogram to the corner of the image
axins = inset_axes(ax[iir,iwv], width="25%", height="25%", loc=4, borderpad=1)
data = img.ravel()
axins.hist(data,bins=bin_num,range=(0,largest),log=True,histtype='step')
axins.tick_params(labelleft=False, labelbottom=False)
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def contrast_heatmap(self):
f,ax = plt.subplots()
ims = ax.imshow(self.contrast_tape)
ax.set_yticks(np.arange(len(self.coords['wvs'])), self.coords['wvs'])
ax.set_xticks(np.arange(len(self.coords['irs'])), self.coords['irs'])
ax.set_ylabel("Wavelength (nm)")
ax.set_xlabel("Imaging Round")
ax.set_title(f'FOV: {self.fov_name}; Z: {self.z_name}')
plt.colorbar(ims)
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def _brightness_through_z(self):
"""This is deprecated.
"""
#Take the image stack and do a max projection from all the pixels through z
mip_z_stack = self.imgstack.max(axis=4)
largest = mip_z_stack.max()
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
plt.suptitle(f'FOV: {self.fov_name}')
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
bin_num = int(largest//2)
data = mip_z_stack[:,:,iwv,iir]
self.contrast_tape[iwv,iir] = self.calc_HS_metric(data)
flat_data = data.ravel()
ax[iir,iwv].hist(flat_data,bins=bin_num,range=(0,largest),log=True,histtype='step')
ax[iir,iwv].text(0.5, 0.5, f'HS:{self.contrast_tape[iwv,iir]}',
ha="center", va="center",
transform=ax[iir,iwv].transAxes)
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def _brightness_through_z_on_images(self):
"""This is Deprecated.
"""
mip_z_stack = self.imgstack.max(axis=4)
max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1).max(axis=1)
val_range = max_wv_val*0.9
largest = mip_z_stack.max()
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
plt.suptitle(f'FOV: {self.fov_name}')
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
img = mip_z_stack[:,:,iwv,iir]
bin_num = int(largest//2)
ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
# Add histogram to the corner of the image
axins = inset_axes(ax[iir,iwv], width="25%", height="25%", loc=4, borderpad=1)
data = img.flatten()
axins.hist(data,bins=bin_num,range=(0,largest),log=True,histtype='step')
axins.tick_params(labelleft=False, labelbottom=False)
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
def _preview_images(self):
"""This is deprecated.
"""
mip_z_stack = self.imgstack.max(axis=4)
max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1).max(axis=1)
val_range = max_wv_val*0.9#self.imgstack.max()*0.90
f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
plt.suptitle(f'FOV: {self.fov_name}')
if not isinstance(ax,np.ndarray):
ax = np.array([ax])
if len(ax.shape)==1:
if len(self.coords['irs'])==1:
ax=ax[np.newaxis,:]
elif len(self.coords['wvs'])==1:
ax=ax[:,np.newaxis]
for iwv,wv in enumerate(self.coords['wvs']):
for iir,ir in enumerate(self.coords['irs']):
img = mip_z_stack[:,:,iwv,iir]
ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
if iwv==0:
ax[iir,iwv].set_ylabel(f'ir:{ir}')
if iir==0:
ax[iir,iwv].set_title(f'channel: {wv}nm')
plt.tight_layout()
self.pdf.savefig()
plt.close(f)
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