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 feat_scale_change_btn_fn(self):
self.feat_scale_change_btn.setEnabled(False)
print('Training Features')
processImgs()
v2.update_density_fn()
self.feat_scale_change_btn.setEnabled(True)
self.image_status_text.showMessage('Model Trained. Continue adding samples, or click \'Save Training Model\'. ')
self.save_model_btn.setEnabled(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 predShowFn(self):
#Captures the button event.
v2.eval_pred_show_fn(par_obj.curr_img,par_obj,self)