# Code adapted from Tensorflow Object Detection Framework
# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
# Tensorflow Object Detection Detector

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

# Parse command line arguments
parser = argparse.ArgumentParser(
    description='Test CharNet performance against Google Vision API results')
parser.add_argument("video_path",
                    metavar="video_path",
                    help="path/to/process/video/")

parser.add_argument("-threshold", required = True,
                    metavar="object detection confidence threshold",
                    help="object detection confidence threshold")

args = parser.parse_args()


class DetectorAPI:
    def __init__(self, path_to_ckpt):
        self.path_to_ckpt = path_to_ckpt

        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)
                tf.import_graph_def(od_graph_def, name='')

        self.default_graph = self.detection_graph.as_default()
        self.sess = tf.Session(graph=self.detection_graph)

        # Definite input and output Tensors for detection_graph
        self.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.
        self.detection_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.
        self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')

    def processFrame(self, image):
        # Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image, axis=0)
        # Actual detection.
        start_time = time.time()
        (boxes, scores, classes, num) = self.sess.run(
            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
            feed_dict={self.image_tensor: image_np_expanded})
        end_time = time.time()

        print("Elapsed Time:", end_time-start_time)

        im_height, im_width,_ = image.shape
        boxes_list = [None for i in range(boxes.shape[1])]
        for i in range(boxes.shape[1]):
            boxes_list[i] = (int(boxes[0,i,0] * im_height),
                        int(boxes[0,i,1]*im_width),
                        int(boxes[0,i,2] * im_height),
                        int(boxes[0,i,3]*im_width))

        return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])

    def close(self):
        self.sess.close()
        self.default_graph.close()

def make_video(filename):
    import cv2    
    img_array = []
    for index in range(len(os.listdir('./results/'))):
        path = './results/{}_{}.jpg'.format(filename, index)
        if(os.path.exists(path)):
          img = cv2.imread('./results/{}_{}.jpg'.format(filename, index))
          height, width, layers = img.shape
          size = (width,height)
          img_array.append(img)
        else:
          print("path: {} does not exist".format(path))        
    
    
    out = cv2.VideoWriter('./{}_result.mp4'.format(filename),cv2.VideoWriter_fourcc(*'DIVX'), 20, size)
    
    for i in range(len(img_array)):
        out.write(img_array[i])
    out.release()

if __name__ == "__main__":
    import os
    model_path = './faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb'
    odapi = DetectorAPI(path_to_ckpt=model_path)
    threshold = float(args.threshold)
    cap = cv2.VideoCapture(args.video_path)
    name = os.path.splitext(args.video_path)[0].split('/')[-1]
    if not os.path.exists('./results'):
        os.makedirs('./results')
    count = 0
    try:
        while True:
            r, img = cap.read()
            img = cv2.resize(img, (1280, 720))

            boxes, scores, classes, num = odapi.processFrame(img)

            # Visualization of the results of a detection.

            for i in range(len(boxes)):
                # Class 1 represents human
                if classes[i] == 1 and scores[i] > threshold:
                    box = boxes[i]
                    cv2.rectangle(img,(box[1],box[0]),(box[3],box[2]),(255,0,0),2)

            cv2.imwrite('./results/{}_{}.jpg'.format(name,count), img)
            count += 1
    except:   
        make_video(name)

