import numpy as np
import os
import tensorflow as tf
from collections import defaultdict
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

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)

PATH_TO_CKPT = "H:\\python\\models\\research\\object_detection\\ssd_mobilenet_v1_coco_11_06_2017\\frozen_inference_graph.pb"
PATH_TO_LABELS = "H:\\python\\models\\research\\object_detection\\data\\mscoco_label_map.pbtxt"
 

NUM_CLASSES = 90
image_path = "test_images\\image1.jpg"

# 載入模型進記憶體
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=''
 
)
# 載入標籤對映
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)

# 開始檢測
with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        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')

        image = Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        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})
        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,
           use_normalized_coordinates=True,
           line_thickness=8)

        plt.imshow(image_np)
        plt.show()