def evaluate_images(self):
par_obj.feat_arr = {}
par_obj.pred_arr = {}
par_obj.sum_pred = {}
count = -1
for b in par_obj.left_2_calc:
frames = par_obj.frames_2_load[b]
for i in frames:
v2.im_pred_inline_fn(par_obj,
self,
inline=True,
outer_loop=b,
inner_loop=i,
count=count)
v2.evaluate_forest(par_obj,
self,
False,
0,
inline=True,
outer_loop=b,
inner_loop=i,
count=count)
count = count + 1
v2.apply_correction(par_obj)
self.save_output_data_btn.setEnabled(True)
self.image_status_text.showMessage('Status: evaluation finished.')
par_obj.eval_load_im_win_eval = True
v2.eval_pred_show_fn(par_obj.curr_img, par_obj, self)
def evaluate_images(self):
par_obj.feat_arr = {}
par_obj.pred_arr = {}
par_obj.sum_pred = {}
count = -1
for b in par_obj.left_2_calc:
frames =par_obj.frames_2_load[b]
for i in frames:
v2.im_pred_inline_fn(par_obj, self,inline=True,outer_loop=b,inner_loop=i,count=count)
v2.evaluate_forest(par_obj,self, False, 0,inline=True,outer_loop=b,inner_loop=i,count=count)
count = count+1
v2.apply_correction(par_obj)
self.save_output_data_btn.setEnabled(True)
self.image_status_text.showMessage('Status: evaluation finished.')
par_obj.eval_load_im_win_eval = True
v2.eval_pred_show_fn(par_obj.curr_img, par_obj,self)
def processImgs(self):
"""Loads images and calculates the features."""
#Resets everything should this be another patch of images loaded.
imgs =[]
gt_im_sing_chgs =[]
fmStr = self.linEdit_Frm.text()
par_obj.feat_arr ={}
par_obj.pred_arr ={}
par_obj.sum_pred ={}
par_obj.frames_2_load ={}
par_obj.left_2_calc =[]
par_obj.saved_ROI =[]
par_obj.saved_dots=[]
par_obj.curr_img = 0
par_obj.eval_load_im_win_eval = False
if self.r0.isChecked():
par_obj.feature_type = 'basic'
if self.r1.isChecked():
par_obj.feature_type = 'fine'
#Now we commit our options to our imported files.
if par_obj.file_ext == 'png':
for i in range(0,par_obj.file_array.__len__()):
par_obj.left_2_calc.append(i)
par_obj.frames_2_load[i] = [0]
self.image_status_text.showMessage('Status: Loading Images. Loading Image Num: '+str(par_obj.file_array.__len__()))
v2.im_pred_inline_fn(par_obj, self)
elif par_obj.file_ext =='tiff' or par_obj.file_ext =='tif':
if par_obj.tiff_file.maxFrames>1:
for i in range(0,par_obj.file_array.__len__()):
par_obj.left_2_calc.append(i)
try:
np.array(list(self.hyphen_range(fmStr)))-1
par_obj.frames_2_load[i] = np.array(list(self.hyphen_range(fmStr)))-1
except:
self.image_status_text.showMessage('Status: The supplied range of image frames is in the wrong format. Please correct and click confirm images.')
return
self.image_status_text.showMessage('Status: Loading Images.')
v2.im_pred_inline_fn(par_obj, self)
else:
for i in range(0,par_obj.file_array.__len__()):
par_obj.left_2_calc.append(i)
par_obj.frames_2_load[i] = [0]
v2.im_pred_inline_fn(par_obj, self)
count = 0
for b in par_obj.left_2_calc:
frames =par_obj.frames_2_load[b]
for i in frames:
count = count+1
par_obj.test_im_start= 0
par_obj.test_im_end= count
for i in par_obj.left_2_calc:
im_array = np.zeros((par_obj.height,par_obj.width))
par_obj.dense_array[i]=im_array
self.image_status_text.showMessage('Status: Images loaded. Click \'Goto Training\'')
self.selIntButton.setEnabled(True)
par_obj.imgs = imgs
