def send_imgs(img_list):
global got_speech
if img_list is None or len(img_list) == 0:
return
detected_objects_list = list()
for index, img in enumerate(img_list):
cv2.imshow('Img ' + str(index), img)
cv2.waitKey(1)
rows, cols, d = img.shape
detected_object = Detected_Object()
detected_object.id = index
detected_object.image = CvBridge().cv2_to_imgmsg(img, encoding="passthrough")
detected_object.center_x = rows / 2
detected_object.center_y = cols / 2
hog, image_hog = get_img_hog(img)
detected_object.features.hog_histogram = hog
detected_object.hog_image = CvBridge().cv2_to_imgmsg(image_hog, encoding="passthrough")
colors_histo, object_shape = getpixelfeatures(img)
detected_object.features.colors_histogram = colors_histo.tolist()
# detected_object.features.shape_histogram = object_shape.tolist()
detected_objects_list.append(detected_object)
# here dumps the sentences describing the shown object (only works with one object at a time and a known object)
orientation.automatic_descriptor.automatic_descriptor(orientation.machinelearning.label_pred(img), detected_object)
if got_speech == 0:
return
detected_objects_list_publisher.publish(detected_objects_list)
got_speech = 0
