# coding: utf-8

# # Object Detection Demo
# Welcome to the object detection inference walkthrough!This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start.

# # Imports

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import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

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("..")#使python遍歷函式時，包括上一層objection裡的API。
from object_detection.utils import ops as utils_ops
#if tf.__version__ < '1.4.0':
    #raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')


# ## Env setup

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# This is needed to display the images.
get_ipython().run_line_magic('matplotlib', 'inline')
#魔法函式，有了他可以省掉plt.show()


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

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from utils import label_map_util

from utils import visualization_utils as vis_util


# # Model preparation準備 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` 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.

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# What model to download.
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
#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'
PATH_TO_CKPT="/home/jdmking/jupyter_notebook/0918_same.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="/home/jdmking/jupyter_notebook/num_label_map.pbtxt"

NUM_CLASSES = 21


# ## Download Model

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#opener = urllib.request.URLopener()#開啟網址URL,這可以是一個字串或一個 Request物件。
#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)#開啟網址，儲存在MOEDL_FILE中。
#tar_file = tarfile.open(MODEL_FILE)#解壓tar_file為<tarfile.TarFile object at 0x7f57e8055358>
#for file in tar_file.getmembers():#file為<TarInfo 'ssd_mobilenet_v1_coco_2017_11_17' at 0x7f57e8037818>
    #file_name = os.path.basename(file.name)#獲取對應路徑的檔名ssd_mobilenet_v1_coco_2017_11_17
    #if 'frozen_inference_graph.pb' in file_name:
        #tar_file.extract(file, os.getcwd())#解壓放在當前資料夾，名字為file


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

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detection_graph = tf.Graph()#建立新的計算圖。
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()#將計算圖進行序列化，用操作
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()#讀pb檔案
        od_graph_def.ParseFromString(serialized_graph)#解析檔案,解析為結構化資料。
        tf.import_graph_def(od_graph_def, name='')#將圖形從od_graph_def匯入當前的預設Graph


# ## 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

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label_map = label_map_util.load_labelmap(PATH_TO_LABELS)#將pbtxt進行proto編譯
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)#將種類變成序列號


# ## Helper code

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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

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# 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 = "/home/jdmking/image_data/0731/0731_images"
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR,filename) for filename in 
                    os.listdir(PATH_TO_TEST_IMAGES_DIR)
                   ]
# Size, in inches, of the output images.
IMAGE_SIZE = (20,20)


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def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            ops = tf.get_default_graph().get_operations()#返回預設圖中的操作節點列表
            all_tensor_names = {output.name for op in ops for output in op.outputs}#先是獲得op，然後獲得output，
        #最後得到output.name
            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)#根據名稱返回tensor資料,Tensor("num_detections:0", dtype=float32)
                                #Tensor("detection_boxes:0", dtype=float32)
                                #Tensor("detection_scores:0", dtype=float32)
                                #Tensor("detection_classes:0", dtype=float32)
            if 'detection_masks' in tensor_dict:
            # The following processing is only for single image(下面的處理只是針對單個影象)
                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)})
            print(output_dict)
      # 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


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for image_path in TEST_IMAGE_PATHS[0:1]:
    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)#影象變為陣列和imread一樣，型別為np.unit8。
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)#用於擴充套件陣列的形狀，新增在0的位置。
    #因為模型的維度為[1,none,none,3].可能因為模型中第一個為batch_size
  # Actual detection.
    output_dict = run_inference_for_single_image(image_np, detection_graph)#output為字典
  # Visualization of the results of a detection.(檢測結果的視覺化。)
    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)
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)