import sys
import time
import numpy as np
import tensorflow as tf
import cv2

# from utils import label_map_util
# from utils import visualization_utils_color as vis_util

class TensoflowFaceDector(object):
    def __init__(self, PATH_TO_CKPT):
        """Tensorflow detector
        """
        self.inference_list = []
        self.count = 0

        self.detection_graph = tf.Graph()
        with self.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='')


        with self.detection_graph.as_default():
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            self.sess = tf.Session(graph=self.detection_graph, config=config)
            self.windowNotSet = True


    def run(self, image):
        """image: bgr image
        return (boxes, scores, classes, num_detections)
        """
        image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        boxes = self.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.
        scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
        # Actual detection.
        start_time = time.time()
        (boxes, scores, classes, num_detections) = self.sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        elapsed_time = time.time() - start_time
        self.inference_list.append(elapsed_time)
        self.count = self.count + 1
        average_inference = sum(self.inference_list)/self.count
        # print('Average inference time: {}'.format(average_inference))

        return (boxes, scores, classes, num_detections)