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 train_model_btn_fn(self):
self.image_status_text.showMessage('Training Ensemble of Decision Trees. ')
v2.update_training_samples_fn(par_obj,0)
self.image_status_text.showMessage('Evaluating Images with the Trained Model. ')
app.processEvents()
v2.evaluate_forest(par_obj,self, False,0)
v2.make_correction(par_obj, 0)
self.image_status_text.showMessage('Model Trained. Continue adding samples, or click \'Save Training Model\'. ')
par_obj.eval_load_im_win_eval = True
self.goto_img_fn(par_obj.curr_img)
self.save_model_btn.setEnabled(True)
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)
