[object detection] opencv와 텐서플로우의 오브젝트 디텍션 api를 혼합해서, vscode에서 이미지가 제대로 나오도록 하는 방법

import tensorflow as tf

import os

import pathlib

import numpy as np

import zipfile

import matplotlib.pyplot as plt

from PIL import Image

import cv2

from object_detection.utils import ops as utils_ops

from object_detection.utils import label_map_util

from object_detection.utils import visualization_utils as viz_utils

# 내 로컬에 설치된 레이블 파일을, 인덱스와 연결시킨다.

PATH_TO_LABELS = 'C:\\Users\\405\\Documents\\TensorFlow\\models\\research\\object_detection\\data\\mscoco_label_map.pbtxt'

category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS)

print(category_index)

# 모델 로드하는 함수.

# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md

# 위의 사이트에서 모델을 가져올수있다.

# /20200711/efficientdet_d0_coco17_tpu-32.tar.gz

# Download and extract model

def download_model(model_name, model_date):

    base_url = 'http://download.tensorflow.org/models/object_detection/tf2/'

    model_file = model_name + '.tar.gz'

    model_dir = tf.keras.utils.get_file(fname=model_name,

                                        origin=base_url + model_date + '/' + model_file,

                                        untar=True)

    return str(model_dir)

MODEL_DATE = '20200711'

MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'

PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE)

def load_model(model_dir) :

    model_full_dir = model_dir + "/saved_model"

    # Load saved model and build the detection function

    detection_model = tf.saved_model.load(model_full_dir)

    return detection_model

detection_model = load_model(PATH_TO_MODEL_DIR)

# print()

# print(detection_model.signatures['serving_default'].inputs)

# print()

# print(detection_model.signatures['serving_default'].output_dtypes)

# print()

# print(detection_model.signatures['serving_default'].output_shapes)

# 우리가 가지고 있는 이미지 경로에서 이미지를 가져오는 코드

PATH_TO_IMAGE_DIR = pathlib.Path('data\\images')

IMAGE_PATHS = list( PATH_TO_IMAGE_DIR.glob('*.jpg') )

print(IMAGE_PATHS)

# 이미지 경로에 있는 이미지를, 넘파이 행렬로 변경해주는 함수

def load_image_into_numpy_array(path):  

    print(str(path))

    return cv2.imread(str(path))

for image_path in IMAGE_PATHS:

    print('Running inference for {}... '.format(image_path), end='')

    image_np = load_image_into_numpy_array(image_path)

   

    # Things to try:

    # Flip horizontally

    # image_np = np.fliplr(image_np).copy()

    # Convert image to grayscale

    # image_np = np.tile(

    #     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)

    # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.

    input_tensor = tf.convert_to_tensor(image_np)

    # The model expects a batch of images, so add an axis with `tf.newaxis`.

    input_tensor = input_tensor[tf.newaxis, ...]

    # input_tensor = np.expand_dims(image_np, 0)

    detections = detection_model(input_tensor)

#     print(detections)

    # All outputs are batches tensors.

    # Convert to numpy arrays, and take index [0] to remove the batch dimension.

    # We're only interested in the first num_detections.

    num_detections = int(detections.pop('num_detections'))

    detections = {key: value[0, :num_detections].numpy()

                   for key, value in detections.items()}

    detections['num_detections'] = num_detections

    # detection_classes should be ints.

    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

   

    print(detections)

    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(

          image_np_with_detections,

          detections['detection_boxes'],

          detections['detection_classes'],

          detections['detection_scores'],

          category_index,

          use_normalized_coordinates=True,

          max_boxes_to_draw=200,

          min_score_thresh=.30,

          agnostic_mode=False)

    cv2.imshow(str(image_path) , image_np_with_detections)

cv2.waitKey(0)

cv2.destroyAllWindows()