How to return values from For Loop via return in a function

[AykutRobotics]

Oh wait. I didn't remove the brackets in the logic statements:

def main():
    c = detected_objects_2()
 
    for value in c:
        if value == "turnLeft":
            print("Turn Left is working!!!")
        elif value == "turnRight":
            print("Turn Right is working!!!")
        else:
            print("NO DETECTION at all!!!")  
  
main()

The result is still same which nothing appears :(

This is the whole code of the tensorflow to detect objects in the test image folder:

import matplotlib
matplotlib.use('Agg')
# # Imports

# In[1]:


import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')


# ## Env setup

# In[ ]:


# This is needed to display the images.
#get_ipython().run_line_magic('matplotlib', 'inline')


# ## Object detection imports
# Here are the imports from the object detection module.

# In[ ]:


from utils import label_map_util

from utils import visualization_utils as vis_util


# # Model preparation 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.  
# 
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

# In[ ]:


# What model to download.
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_NAME = 'trafficSign_turnLeft_turnRight_graph'
#MODEL_FILE = MODEL_NAME + '.tar.gz'
#DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
#PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
PATH_TO_LABELS = os.path.join('data', 'labelmap.pbtxt')

#general_object_detection = 'ssd_mobilenet_v1_coco_2017_11_17'
#trafficSign_object_detection =''

# ## Download Model

# In[ ]:


#opener = urllib.request.URLopener()
#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
#tar_file = tarfile.open(MODEL_FILE)
#for file in tar_file.getmembers():
  #file_name = os.path.basename(file.name)
  #if 'frozen_inference_graph.pb' in file_name:
    #tar_file.extract(file, os.getcwd())


# ## Load a (frozen) Tensorflow model into memory.

# In[ ]:


detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')


# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

# In[ ]:


category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)


# ## Helper code

# In[ ]:


def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


# # Detection

# In[ ]:


# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'test{}.jpg'.format(i)) for i in range(1, 7) ]
#TEST_IMAGE_PATHS = os.path.join(PATH_TO_TEST_IMAGES_DIR, 'test1.jpg')

#cwd = os.getcwd()
#files = os.listdir(cwd)
#print("Files in '%s': %s" % (cwd, files))

IMAGE_NAME = 'test2.jpg'

# Grab path to current working directory

CWD_PATH = os.getcwd()

# Path to image

PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)


# In[ ]:


def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: np.expand_dims(image, 0)})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.uint8)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict


def detected_objects_2():
    b_values = []
    for image_path in TEST_IMAGE_PATHS:
        image = Image.open(image_path)
      
        folder_path = "test_images/" #folder path to your images 
      
        File_Lst = []
      
        for file in os.listdir(folder_path):
            File_Lst.append(file)
          
        dog_index = File_Lst.index('image1.jpg')           
        dog_str = File_Lst[dog_index]
      
        img = cv2.imread(folder_path + dog_str )
        cv2.destroyAllWindows()         
        cv2.imshow("Press KEYS to know which direction you want to go with your robot", img)
      
        image_np = load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = run_inference_for_single_image(image_np, detection_graph)
      
        a, b = vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          output_dict['detection_boxes'],
          output_dict['detection_classes'],
          output_dict['detection_scores'],
          category_index,
          instance_masks=output_dict.get('detection_masks'),
          use_normalized_coordinates=True,
          line_thickness=8)
        b_values.append(b)

        plt.figure(figsize=IMAGE_SIZE)
        cv2.destroyAllWindows()
        cv2.imshow("Object Detector", image_np)

        #print(b_values)  
        k = cv2.waitKey(0)
        if k == ord('a'): # wait for 'a' key to upload traffic signs one by one
            cv2.destroyAllWindows()
            cv2.imshow("Object Detector", image_np)
            
        elif k == ord('s'):
            cv2.waitKey(0)
            cv2.destroyAllWindows()
            break    
    return b_values


def main():
  c = detected_objects_2()
  
  for value in c:
    if value == "turnLeft":
      print("Turn Left is working!!!")
    elif value == "turnRight":
      print("Turn Right is working!!!")
    else:
      print("NO DETECTION at all!!!")  
   
main()

The code seems correct but why it doesn't return anything I don't understand.