IR_image_tools.py 11.5 KB
 Holger Niemann committed May 17, 2018 1 2 3 4 ``````# -*- coding: utf-8 -*- """ Created on Wed May 9 14:56:32 2018 `````` Holger Niemann committed Jun 06, 2018 5 ``````@author: Holger Niemann, Peter Drewelow, Yu Gao `````` Holger Niemann committed May 17, 2018 6 7 8 9 10 11 12 13 14 15 16 `````` mainly to clean up the downloadversionIRdata code Tools for: checking IR images, calculate gain and offset again from check backgroundframes check coldframes ... """ import numpy as np import matplotlib.pyplot as plt `````` Holger Niemann committed Jun 06, 2018 17 18 19 20 21 22 ``````from IR_config_constants import portcamdict,IRCamRefImagespath import h5py from os.path import join, basename import glob `````` Holger Niemann committed May 17, 2018 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 `````` def bestimmtheitsmaß_general(data,fit): R=0 if len(fit)==len(data): mittel=np.sum(data)/len(data) qam=quad_abweich_mittel(fit,mittel) R=qam/(qam+quad_abweich(data,fit)) else: print("Arrays must have same dimensions") return R def quad_abweich_mittel(data,mittel): R=0 for i in data: R=R+(i-mittel)**2 return R def quad_abweich(data,fit): R=0 if len(fit)==len(data): for i in range(len(data)): R=R+(data[i]-fit[i])**2 else: print("Arrays must have same dimensions") return R def check_coldframe(coldframe,references=None,threshold=0.5,plot_it=False): ''' return true/false and the quality factor ''' shapi=np.shape(coldframe) ##function (np.arange(0,768)-384)**(2)/900-50 datasets=[] for i in [int(shapi[1]//4),int(shapi[1]//2),int(shapi[1]//4*3)]: dataline=coldframe[0:shapi[0],i] datasets.append(dataline-np.mean(dataline)) if references==None: references=[] for dat in datasets: mini=np.mean(dat[shapi[0]/2-50:shapi[0]/2+50]) a=(np.mean(dat[0:50])-mini)/(int(shapi[0]/2))**2 reference=a*(np.arange(0,shapi[0])-int(shapi[0]/2))**(2)+mini references.append(reference) bestimmtheit=[] if plot_it: plt.figure() plt.imshow(coldframe,vmin=np.mean(coldframe)-500,vmax=np.mean(coldframe)+500) plt.figure() for i_dat in range(len(datasets)): dat=datasets[i_dat] reference=references[i_dat] bestimmtheit.append(bestimmtheitsmaß_general(dat,reference)) if plot_it: plt.plot(dat,label='data') plt.plot(reference,label='reference') # print(int(shapi[0]/2),1*(np.max(datasets[-1])-mini),mini) plt.legend() if np.mean(bestimmtheit)>threshold: return True,bestimmtheit else: return False,bestimmtheit def check_coldframe_by_refframe(coldframe,reference_frame,threshold=0.8,plot_it=False): references=[] shapi=np.shape(reference_frame) `````` Holger Niemann committed Jun 06, 2018 88 `````` for i in [int(shapi[1]//5),int(shapi[1]//2),int(shapi[1]//4*3)]: `````` Holger Niemann committed May 17, 2018 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 `````` dataline=reference_frame[0:shapi[0],i] references.append(dataline-np.mean(dataline)) return check_coldframe(coldframe,references,threshold,plot_it) def check_backgroundframe(backgroundframe,threshold=50): ''' return true or false ''' shapi=np.shape(backgroundframe) valid=True dataset=[] for i in [int(shapi[1]//4),int(shapi[1]//2),int(shapi[1]//4*3)]: referenceline=backgroundframe[0:shapi[0],i] meanref=referenceline-np.mean(referenceline) dataset.append(np.max(meanref)-np.min(meanref)) if np.mean(dataset)threshold)) ) bad_pixels = (bad_pixels).tolist() bad_pixels = [tuple(l) for l in bad_pixels] if plot_it: plt.figure() plt.imshow(frame) for i in range(len(bad_pixels)): plt.scatter(bad_pixels[i][1],bad_pixels[i][0],c='None') plt.show() return bad_pixels def correct_images(images,badpixels): if type(badpixels)!=int: for i in range(len(images)): images[i]=(restore_pixels(images[i],np.invert(badpixels==1))).astype(np.float32) print("done") return images `````` Holger Niemann committed Jun 06, 2018 154 ``````def restore_pixels(frame, bad_pixel):# code from Peter from JET `````` Holger Niemann committed May 17, 2018 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 `````` # make sure bad pixel are provided as mask and list if type(bad_pixel) is list: blist = bad_pixel bmask = np.ones(frame.shape,dtype=bool) for pix in bad_pixel: bmask[pix] = False bmask = np.invert(bmask) else: bmask = np.invert(bad_pixel) x,y = np.where(bmask) blist = list(zip(x,y)) # print('restore_pixels() working on: ',blist) # prepare internal result frames resframe = frame.astype(float) # ----------------------------------- # restore by list # height = np.shape(frame)[0] width = np.shape(frame)[1] for pos in blist: # assume four neighbouring, non-bad pixels exist n_neighbours = 4 # -- top neighbour -- if pos[0] == 0: # out of bounds top = 0 n_neighbours -= 1 else: i_t = pos[0] - 1 while (bmask[i_t,pos[1]] & (i_t >= 0)): i_t -= 1 if i_t >= 0: top = frame[i_t,pos[1]] else: # out of bounds top = 0 n_neighbours -= 1 #print('top: ',i_t, top) # -- bottom neighbour -- if pos[0] == height-1: # out of bounds bottom = 0 n_neighbours -= 1 else: i_b = pos[0] + 1 while (bmask[i_b,pos[1]] & (i_b <= height-2)): i_b += 1 if i_b >= 0: bottom = frame[i_b,pos[1]] else: # out of bounds bottom = 0 n_neighbours -= 1 #print('bottom: ',i_b, bottom) # -- left neighbour -- if pos[1] == 0: # out of bounds left = 0 n_neighbours -= 1 else: i_l = pos[1] - 1 while (bmask[pos[0],i_l] & (i_l >= 0)): i_l -= 1 if i_l >= 0: left = frame[pos[0],i_l] else: # out of bounds left = 0 n_neighbours -= 1 #print('left: ',i_l, left) # -- right neighbour -- if pos[1] == width-1: # out of bounds right = 0 n_neighbours -= 1 else: i_r = pos[1] + 1 while (bmask[pos[0],i_r] & (i_r <= width-2)): i_r += 1 if i_r >= 0: right = frame[pos[0],i_r] else: # out of bounds right = 0 n_neighbours -= 1 #print('right: ',i_r, right) # averaging if n_neighbours > 0: #print('original value: ',frame[pos[0],pos[1]]) resframe[pos[0],pos[1]] = (top + bottom + left + right)/n_neighbours #print('average of ',n_neighbours,' neighbours: ',frame1[pos[0],pos[1]]) else: print('ERROR: no adjacent pixel found!') return resframe def generate_new_hot_image(cold,reference_cold,reference_hot): if cold==None or reference_cold==None or reference_hot==None: raise Exception("Cannot Calculate new Hot image, if images are missing!") else: return reference_hot+(cold-reference_cold) `````` Holger Niemann committed Jun 06, 2018 256 ``````def calculate_gain_offset_image_pix(cold_image,hot_image=None,reference_cold=None,reference_hot=None,bose=1): `````` Holger Niemann committed May 17, 2018 257 258 `````` if hot_image==None: hot_image=generate_new_hot_image(cold_image,reference_cold,reference_hot) `````` Holger Niemann committed Jun 06, 2018 259 260 `````` if bose>0: print("calculate gain and offset") `````` Holger Niemann committed May 17, 2018 261 262 263 264 265 266 267 `````` Sh_ref = hot_image[ ( np.int( np.shape(hot_image)[0] /2 ) ) ][np.int( (np.shape(hot_image)[1] /2 ) ) ] Sc_ref = cold_image[ ( np.int( (np.shape(cold_image)[0]) /2 ) ) ][( np.int( (np.shape(cold_image)[1]) /2 ) ) ] Gain_rel = ( Sh_ref - Sc_ref ) / ( hot_image - cold_image) Off_h_rel = Sh_ref - hot_image*Gain_rel Off_c_rel = Sc_ref - cold_image*Gain_rel Offset_rel = ( Off_h_rel + Off_c_rel ) /2 return Gain_rel,Offset_rel `````` Holger Niemann committed Jun 06, 2018 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 `````` def calculate_gain_offset_image(cold_image,hot_image=None,reference_cold=None,reference_hot=None,bose=1): if hot_image==None: hot_image=generate_new_hot_image(cold_image,reference_cold,reference_hot) if bose>0: print("calculate gain and offset") # Sh_ref = hot_image[ ( np.int( np.shape(hot_image)[0] /2 ) ) ][np.int( (np.shape(hot_image)[1] /2 ) ) ] # Sc_ref = cold_image[ ( np.int( (np.shape(cold_image)[0]) /2 ) ) ][( np.int( (np.shape(cold_image)[1]) /2 ) ) ] print(hot_image[( np.int( np.shape(hot_image)[0]/2) )-2: (np.int( np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3 ]) print(cold_image[( np.int( np.shape(hot_image)[0]/2) )-2: (np.int( np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3 ]) Sh_ref = np.mean( hot_image[( np.int( np.shape(hot_image)[0]/2) )-2: (np.int( np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3 ]) Sc_ref = np.mean(cold_image[( np.int( np.shape(cold_image)[0]/2) )-2: (np.int( np.shape(cold_image)[0]/2))+3,np.int((np.shape(cold_image)[1]/2))-2:np.int((np.shape(cold_image)[1]/2))+3 ]) Gain_rel = ( Sh_ref - Sc_ref ) / ( hot_image - cold_image) Off_h_rel = Sh_ref - hot_image*Gain_rel Off_c_rel = Sc_ref - cold_image*Gain_rel Offset_rel = ( Off_h_rel + Off_c_rel ) /2 return Gain_rel,Offset_rel #%% functions from Yu Gao """ functions by Yu Gao""" def load_ref_images(port, exposuretime): ''' load the reference cold and hot frame during calibration from local files. @port: e.g. 'AEF10' @exposuretime: int number. ''' cameraname = portcamdict['OP1.2a'][port] foldername = cameraname.split('_')[0] + '_' + cameraname.split('_')[2] scanpath = join(IRCamRefImagespath, foldername) coldref, hotref = [], [] for filename in glob.iglob(scanpath + '\*' + str(int(exposuretime)) + 'us.h5', recursive=True): if 'hot' in filename: print (filename) with h5py.File(filename, 'r') as h5in: hotref = h5in[basename(filename)].value elif 'cold' in filename: print (filename) with h5py.File(filename, 'r') as h5in: coldref = h5in[basename(filename)].value return coldref, hotref def reconstruct_coldframe (exposuretime, sT, a, bnew, coldref): cirebuild = a * sT + bnew * exposuretime + coldref return cirebuild``````