accuracy improvement for counting

Author: ahmetozlu

api/object_counting_api.py

@@ -119,7 +119,7 @@ def cumulative_object_counting_x_axis(input_video, detection_graph, category_ind
             cap.release()
             cv2.destroyAllWindows()
 
-def cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name):
+def cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name, targeted_objects=None):
         total_passed_objects = 0        
 
         # input video
@@ -178,6 +178,7 @@ def cumulative_object_counting_y_axis(input_video, detection_graph, category_ind
                                                                                                              np.squeeze(classes).astype(np.int32),
                                                                                                              np.squeeze(scores),
                                                                                                              category_index,
+                                                                                                             targeted_objects = targeted_objects,
                                                                                                              y_reference = roi,
                                                                                                              deviation = deviation,
                                                                                                              use_normalized_coordinates=True,

utils/color_recognition_module/test.data

@@ -1 +1 @@
-212,255,127
+0,0,0

utils/visualization_utils.py

@@ -919,10 +919,10 @@ def visualize_boxes_and_labels_on_image_array_y_axis(current_frame_number,
     if(targeted_objects == None):
       counting_mode = counting_mode + str(display_str_list)
 
-    elif(targeted_objects in display_str_list[0]):
+    elif(display_str_list[0].split(":")[0] in targeted_objects):
       counting_mode = counting_mode + str(display_str_list)
 
-    if ((targeted_objects != None) and (targeted_objects in display_str_list[0])):
+    if ((targeted_objects != None) and (display_str_list[0].split(":")[0] in targeted_objects)):
 	    if instance_masks is not None:
 	      draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
 

vehicle_counting.py

@@ -17,8 +17,9 @@
 detection_graph, category_index = backbone.set_model('ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt')
 
 is_color_recognition_enabled = 1 # set it to 1 for enabling the color prediction for the detected objects
-roi = 185 # roi line position
-deviation = 2 # the constant that represents the object counting area
+roi = 237 # roi line position
+deviation = 4.5 # the constant that represents the object counting area
 custom_object_name = 'Vehicle' # set it to your custom object name
+targeted_objects_name = "car, truck"
 
-object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name) # counting all the objects
+object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name, targeted_objects_name) # counting all the objects