Raspberry pi 4B std::bad_alloc

Hi I embedded trained model(deep learning) in raspberry pi 4B(4GB) not a connection with server. I just inserted python scripts in raspberry pi.
I tested it by putty and vnc viewer but It resulted some problems bad alloc.
Actually i don’t know why but i think the small ram size is the reason.
And also, trained model may be so heavy…

error
terminate called after throwing an instance of ‘std::bad_alloc’
what(): std::bad_alloc
Aborted

test.py code below.

import numpy as np
import os
import six.moves.urllib as urllib
import tarfile
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image
from os import path
from utils import label_map_util
from utils import visualization_utils as vis_util
import time
import cv2


def detect_red(img, Threshold=0.01):
    """
    detect red and yellow
    :param img:
    :param Threshold:
    :return:
    """

    desired_dim = (30, 90) # width, height
    img = cv2.resize(np.array(img), desired_dim, interpolation=cv2.INTER_LINEAR)
    img_hsv=cv2.cvtColor(img, cv2.COLOR_RGB2HSV)

    # lower mask (0-10)
    lower_red = np.array([0,70,50])
    upper_red = np.array([10,255,255])
    mask0 = cv2.inRange(img_hsv, lower_red, upper_red)

    # upper mask (170-180)
    lower_red = np.array([170,70,50])
    upper_red = np.array([180,255,255])
    mask1 = cv2.inRange(img_hsv, lower_red, upper_red)

    # red pixels' mask
    mask = mask0+mask1

    # Compare the percentage of red values
    rate = np.count_nonzero(mask) / (desired_dim[0] * desired_dim[1])

    if rate > Threshold:
        return True
    else:
        return False

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)




def read_traffic_lights(image, boxes, scores, classes, max_boxes_to_draw=20, min_score_thresh=0.5, traffic_ligth_label=10):
    im_width, im_height = image.size
    red_flag = False
    for i in range(min(max_boxes_to_draw, boxes.shape[0])):
        if scores[i] > min_score_thresh and classes[i] == traffic_ligth_label:
            ymin, xmin, ymax, xmax = tuple(boxes[i].tolist())
            (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                          ymin * im_height, ymax * im_height)
            crop_img = image.crop((left, top, right, bottom))

            if detect_red(crop_img):
                red_flag = True

    return red_flag


def plot_origin_image(image_np, boxes, classes, scores, category_index):

    # Size of the output images.
    IMAGE_SIZE = (12, 8)
    vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      np.squeeze(boxes),
      np.squeeze(classes).astype(np.int32),
      np.squeeze(scores),
      category_index,
      min_score_thresh=.5,
      use_normalized_coordinates=True,
      line_thickness=3)
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)

    # save augmented images into hard drive
    # plt.savefig( 'output_images/ouput_' + str(idx) +'.png')
    plt.show()


def detect_traffic_lights(PATH_TO_TEST_IMAGES_DIR, MODEL_NAME, Num_images, plot_flag=False):
    """
    Detect traffic lights and draw bounding boxes around the traffic lights
    :param PATH_TO_TEST_IMAGES_DIR: testing image directory
    :param MODEL_NAME: name of the model used in the task
    :return: commands: True: go, False: stop
    """

    #--------test images------
    TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'img_{}.jpg'.format(i)) for i in range(1, Num_images+1) ]


    commands = []

    # What model to download
    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_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

    # List of the strings that is used to add correct label for each box.
    PATH_TO_LABELS = 'mscoco_label_map.pbtxt'

    # number of classes for COCO dataset
    NUM_CLASSES = 90


    #--------Download model----------
    # if path.isdir(MODEL_NAME) is False:
    #     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
    detection_graph = tf.Graph()
    with detection_graph.as_default():
      od_graph_def = tf.GraphDef()
      with tf.gfile.GFile(PATH_TO_CKPT, '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_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map,
                                                                max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)


    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            # Definite input and output Tensors for detection_graph
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            # Each box represents a part of the image where a particular object was detected.
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            # Each score represent how level of confidence for each of the objects.
            # Score is shown on the result image, together with the class label.
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')

            for image_path in TEST_IMAGE_PATHS:
                image = Image.open(image_path)

                # the array based representation of the image will be used later in order to prepare the
                # result image with boxes and labels on it.
                image_np = load_image_into_numpy_array(image)
                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
                image_np_expanded = np.expand_dims(image_np, axis=0)
                # Actual detection.
                (boxes, scores, classes, num) = sess.run(
                  [detection_boxes, detection_scores, detection_classes, num_detections],
                  feed_dict={image_tensor: image_np_expanded})


                red_flag = read_traffic_lights(image, np.squeeze(boxes), np.squeeze(scores), np.squeeze(classes).astype(np.int32))
                if red_flag:
                    print('{}: stop'.format(image_path))  # red or yellow
                    commands.append(False)
                else:
                    print('{}: go'.format(image_path))
                    commands.append(True)

                # Visualization of the results of a detection.
                if plot_flag:
                    plot_origin_image(image_np, boxes, classes, scores, category_index)

    return commands

if __name__ == "__main__":


    Num_images = 1
    PATH_TO_TEST_IMAGES_DIR = './test_images'
    MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'

    commands = detect_traffic_lights(PATH_TO_TEST_IMAGES_DIR, MODEL_NAME, Num_images, plot_flag=False)
    print(commands)

In my opinion, expanding the ram size or searching the more light trained model is the way to solve problems. Is that right?
If not, please tell me how to solve this problem… i spend all day to solve it…:slightly_frowning_face: