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