# pip3 install openvino
 
# Install ONNX Runtime for OpenVINO™ Execution Provider
# pip3 install onnxruntime-openvino==1.11.0
 
# pip3 install -r requirements.txt
# Running the ONNXRuntime OpenVINO™ Execution Provider sample
# python3 yolov4.py --device CPU_FP32 --video classroom.mp4 --model yolov4.onnx

'''
Copyright (C) 2021-2022, Intel Corporation
SPDX-License-Identifier: Apache-2.0
Major Portions of this code are copyright of their respective authors and released under the Apache License Version 2.0:
- onnx, Copyright 2021-2022. For licensing see https://github.com/onnx/models/blob/master/LICENSE
'''

import cv2
import numpy as np
from onnx import numpy_helper
import onnx
import onnxruntime as rt
import os
from PIL import Image
from scipy import special
import colorsys
import random
import argparse
import sys
import time
import platform

if platform.system() == "Windows":
    from openvino import utils
    utils.add_openvino_libs_to_path()

def image_preprocess(image, target_size, gt_boxes=None):

    ih, iw = target_size
    h, w, _ = image.shape

    scale = min(iw/w, ih/h)
    nw, nh = int(scale * w), int(scale * h)
    image_resized = cv2.resize(image, (nw, nh))

    image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
    dw, dh = (iw - nw) // 2, (ih-nh) // 2
    image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized
    image_padded = image_padded / 255.

    if gt_boxes is None:
        return image_padded

    else:
        gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw
        gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh
        return image_padded, gt_boxes

def postprocess_bbbox(pred_bbox):
    '''define anchor boxes'''
    for i, pred in enumerate(pred_bbox):
        conv_shape = pred.shape
        output_size = conv_shape[1]
        conv_raw_dxdy = pred[:, :, :, :, 0:2]
        conv_raw_dwdh = pred[:, :, :, :, 2:4]
        xy_grid = np.meshgrid(np.arange(output_size), np.arange(output_size))
        xy_grid = np.expand_dims(np.stack(xy_grid, axis=-1), axis=2)

        xy_grid = np.tile(np.expand_dims(xy_grid, axis=0), [1, 1, 1, 3, 1])
        xy_grid = xy_grid.astype(float)

        pred_xy = ((special.expit(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * STRIDES[i]
        pred_wh = (np.exp(conv_raw_dwdh) * ANCHORS[i])
        pred[:, :, :, :, 0:4] = np.concatenate([pred_xy, pred_wh], axis=-1)

    pred_bbox = [np.reshape(x, (-1, np.shape(x)[-1])) for x in pred_bbox]
    pred_bbox = np.concatenate(pred_bbox, axis=0)
    return pred_bbox

def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold):
    '''remove boundary boxs with a low detection probability'''
    valid_scale=[0, np.inf]
    pred_bbox = np.array(pred_bbox)

    pred_xywh = pred_bbox[:, 0:4]
    pred_conf = pred_bbox[:, 4]
    pred_prob = pred_bbox[:, 5:]

    # # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax)
    pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5,
                                pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1)
    # # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org)
    org_h, org_w = org_img_shape
    resize_ratio = min(input_size / org_w, input_size / org_h)

    dw = (input_size - resize_ratio * org_w) / 2
    dh = (input_size - resize_ratio * org_h) / 2

    pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio
    pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio

    # # (3) clip some boxes that are out of range
    pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]),
                                np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1)
    invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3]))
    pred_coor[invalid_mask] = 0

    # # (4) discard some invalid boxes
    bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1))
    scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1]))

    # # (5) discard some boxes with low scores
    classes = np.argmax(pred_prob, axis=-1)
    scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]
    score_mask = scores > score_threshold
    mask = np.logical_and(scale_mask, score_mask)
    coors, scores, classes = pred_coor[mask], scores[mask], classes[mask]

    return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1)

def bboxes_iou(boxes1, boxes2):
    '''calculate the Intersection Over Union value'''
    boxes1 = np.array(boxes1)
    boxes2 = np.array(boxes2)

    boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
    boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])

    left_up       = np.maximum(boxes1[..., :2], boxes2[..., :2])
    right_down    = np.minimum(boxes1[..., 2:], boxes2[..., 2:])

    inter_section = np.maximum(right_down - left_up, 0.0)
    inter_area    = inter_section[..., 0] * inter_section[..., 1]
    union_area    = boxes1_area + boxes2_area - inter_area
    ious          = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)

    return ious

def nms(bboxes, iou_threshold, sigma=0.3, method='nms'):
    """
    :param bboxes: (xmin, ymin, xmax, ymax, score, class)
    Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf
          https://github.com/bharatsingh430/soft-nms
    """
    classes_in_img = list(set(bboxes[:, 5]))
    best_bboxes = []

    for cls in classes_in_img:
        cls_mask = (bboxes[:, 5] == cls)
        cls_bboxes = bboxes[cls_mask]

        while len(cls_bboxes) > 0:
            max_ind = np.argmax(cls_bboxes[:, 4])
            best_bbox = cls_bboxes[max_ind]
            best_bboxes.append(best_bbox)
            cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]])
            iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4])
            weight = np.ones((len(iou),), dtype=np.float32)

            assert method in ['nms', 'soft-nms']

            if method == 'nms':
                iou_mask = iou > iou_threshold
                weight[iou_mask] = 0.0

            if method == 'soft-nms':
                weight = np.exp(-(1.0 * iou ** 2 / sigma))

            cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight
            score_mask = cls_bboxes[:, 4] > 0.
            cls_bboxes = cls_bboxes[score_mask]

    return best_bboxes

def read_class_names(class_file_name):
    '''loads class name from a file'''
    names = {}
    with open(class_file_name, 'r') as data:
        for ID, name in enumerate(data):
            names[ID] = name.strip('\n')
    return names

def draw_bbox(image, bboxes, classes=read_class_names("coco.names"), show_label=True):
    """
    bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
    """

    num_classes = len(classes)
    image_h, image_w, _ = image.shape
    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))

    random.seed(0)
    random.shuffle(colors)
    random.seed(None)

    for i, bbox in enumerate(bboxes):
        coor = np.array(bbox[:4], dtype=np.int32)
        fontScale = 0.5
        score = bbox[4]
        class_ind = int(bbox[5])
        bbox_color = colors[class_ind]
        bbox_thick = int(0.6 * (image_h + image_w) / 600)
        c1, c2 = (coor[0], coor[1]), (coor[2], coor[3])
        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)

        if show_label:
            bbox_mess = '%s: %.2f' % (classes[class_ind], score)
            t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0]
            cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1)
            cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX,
                        fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA)

    return image

def get_anchors(anchors_path, tiny=False):
    '''loads the anchors from a file'''
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = np.array(anchors.split(','), dtype=np.float32)
    return anchors.reshape(3, 3, 2)

#Specify the path to anchors file on your machine
ANCHORS = "./yolov4_anchors.txt"    
STRIDES = [8, 16, 32]
XYSCALE = [1.2, 1.1, 1.05]
ANCHORS = get_anchors(ANCHORS)
STRIDES = np.array(STRIDES)

def parse_arguments():
    parser = argparse.ArgumentParser(description='Object Detection using YOLOv4 in OPENCV using OpenVINO Execution Provider for ONNXRuntime')
    parser.add_argument('--device', default='CPU_FP32', help="Device to perform inference on 'cpu (MLAS)' or on devices supported by OpenVINO-EP [CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16].")
    parser.add_argument('--image', help='Path to image file.')
    parser.add_argument('--video', help='Path to video file.')
    parser.add_argument('--model', help='Path to model.')
    args = parser.parse_args()
    return args
    
def check_model_extension(fp):
  # Split the extension from the path and normalise it to lowercase.
  ext = os.path.splitext(fp)[-1].lower()

  # Now we can simply use != to check for inequality, no need for wildcards.
  if(ext != ".onnx"):
    raise Exception(fp, "is an unknown file format. Use the model ending with .onnx format")
  
  if not os.path.exists(fp):
    raise Exception("[ ERROR ] Path of the onnx model file is Invalid")

def main(): 

    # Process arguments
    args = parse_arguments()

    # Validate model file path
    check_model_extension(args.model)

    # Process inputs
    win_name = 'Object detection using ONNXRuntime OpenVINO Execution Provider using YoloV4 model'
    cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)

    output_file = "yolo_out_py.avi"
    if (args.image):
        # Open the image file
        if not os.path.isfile(args.image):
            print("Input image file ", args.image, " doesn't exist")
            sys.exit(1)
        cap = cv2.VideoCapture(args.image)
        output_file = args.image[:-4]+'_yolo_out_py.jpg'
    elif (args.video):
        # Open the video file
        if not os.path.isfile(args.video):
            print("Input video file ", args.video, " doesn't exist")
            sys.exit(1)
        cap = cv2.VideoCapture(args.video)
        output_file = args.video[:-4]+'_yolo_out_py.avi'
    else:
        # Webcam input
        cap = cv2.VideoCapture(0)

    # Get the video writer initialized to save the output video
    if (not args.image):
        vid_writer = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc('M','J','P','G'), 30, (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
    
    # Check the device information and create a session
    device = args.device
    so = rt.SessionOptions()
    so.log_severity_level = 3
    if(args.device == 'cpu'):
        print("Device type selected is 'cpu' which is the default CPU Execution Provider (MLAS)")
        #Specify the path to the ONNX model on your machine and register the CPU EP
        sess = rt.InferenceSession(args.model, so, providers=['CPUExecutionProvider'])
    else:
        #Specify the path to the ONNX model on your machine and register the OpenVINO EP
        sess = rt.InferenceSession(args.model, so, providers=['OpenVINOExecutionProvider'], provider_options=[{'device_type' : device}])
        print("Device type selected is: " + device + " using the OpenVINO Execution Provider")
        '''
        other 'device_type' options are: (Any hardware target can be assigned if you have the access to it)
        'CPU_FP32', 'GPU_FP32', 'GPU_FP16', 'MYRIAD_FP16', 'VAD-M_FP16'
        '''

    input_name = sess.get_inputs()[0].name
    
    while cv2.waitKey(1) < 0:
        # get frame from the video
        has_frame, frame = cap.read()
        # Stop the program if reached end of video
        if not has_frame:
            print("Done processing !!!")
            print("Output file is stored as ", output_file)
            has_frame=False
            cv2.waitKey(3000)
            # Release device
            cap.release()
            break
            
        input_size = 416
        original_image = frame
        original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
        original_image_size = original_image.shape[:2]

        image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
        image_data = image_data[np.newaxis, ...].astype(np.float32)
    
        outputs = sess.get_outputs()
        output_names = list(map(lambda output: output.name, outputs))

        start = time.time()
        detections = sess.run(output_names, {input_name: image_data})
        end = time.time()
        inference_time = end - start

        pred_bbox = postprocess_bbbox(detections)
        bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25)
        bboxes = nms(bboxes, 0.213, method='nms')
        image = draw_bbox(original_image, bboxes)

        cv2.putText(image,device,(10,20),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1)
        cv2.putText(image,'FPS: {}'.format(1.0/inference_time),(10,40),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1)

        # Write the frame with the detection boxes
        if (args.image):
            cv2.imwrite(output_file, image.astype(np.uint8))
        else:
            vid_writer.write(image.astype(np.uint8))
 
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        cv2.imshow(win_name, image)

if __name__ == "__main__":
  main()

# pip3 install openvino

# Install ONNX Runtime for OpenVINO™ Execution Provider
# pip3 install onnxruntime-openvino==1.11.0

# pip3 install -r requirements.txt
# How to run the sample
# python3 tiny_yolov2_obj_detection_sample.py --h

# Running the ONNXRuntime OpenVINO™ Execution Provider sample
# python3 tiny_yolov2_obj_detection_sample.py --video face-demographics-walking-and-pause.mp4 --model tinyyolov2.onnx --device CPU_FP32

'''
Copyright (C) 2021-2022, Intel Corporation
SPDX-License-Identifier: Apache-2.0
'''

import numpy as np
import onnxruntime as rt
import cv2
import time
import os
import argparse
import platform

if platform.system() == "Windows":
    from openvino import utils
    utils.add_openvino_libs_to_path()

# color look up table for different classes for object detection sample
clut = [(0,0,0),(255,0,0),(255,0,255),(0,0,255),(0,255,0),(0,255,128),
        (128,255,0),(128,128,0),(0,128,255),(128,0,128),
        (255,0,128),(128,0,255),(255,128,128),(128,255,128),(255,255,0),
        (255,128,128),(128,128,255),(255,128,128),(128,255,128),(128,255,128)]

# 20 labels that the tiny-yolov2 model can do the object_detection on
label = ["aeroplane","bicycle","bird","boat","bottle",
         "bus","car","cat","chair","cow","diningtable",
         "dog","horse","motorbike","person","pottedplant",
          "sheep","sofa","train","tvmonitor"]

def parse_arguments():
  parser = argparse.ArgumentParser(description='Object Detection using YOLOv2 in OPENCV using OpenVINO Execution Provider for ONNXRuntime')
  parser.add_argument('--device', default='CPU_FP32', help="Device to perform inference on 'cpu (MLAS)' or on devices supported by OpenVINO-EP [CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16].")
  parser.add_argument('--video', help='Path to video file.')
  parser.add_argument('--model', help='Path to model.')
  args = parser.parse_args()
  return args

def sigmoid(x, derivative=False):
  return x*(1-x) if derivative else 1/(1+np.exp(-x))

def softmax(x):
  score_mat_exp = np.exp(np.asarray(x))
  return score_mat_exp / score_mat_exp.sum(0)

def check_model_extension(fp):
  # Split the extension from the path and normalise it to lowercase.
  ext = os.path.splitext(fp)[-1].lower()

  # Now we can simply use != to check for inequality, no need for wildcards.
  if(ext != ".onnx"):
    raise Exception(fp, "is an unknown file format. Use the model ending with .onnx format")
  
  if not os.path.exists(fp):
    raise Exception("[ ERROR ] Path of the onnx model file is Invalid")

def check_video_file_extension(fp):
  # Split the extension from the path and normalise it to lowercase.
  ext = os.path.splitext(fp)[-1].lower()
  # Now we can simply use != to check for inequality, no need for wildcards.
  
  if(ext == ".mp4" or ext == ".avi" or ext == ".mov"):
    pass
  else:
    raise Exception(fp, "is an unknown file format. Use the video file ending with .mp4 or .avi or .mov formats")
  
  if not os.path.exists(fp):
    raise Exception("[ ERROR ] Path of the video file is Invalid")

def image_preprocess(frame):
  in_frame = cv2.resize(frame, (416, 416))
  preprocessed_image = np.asarray(in_frame)
  preprocessed_image = preprocessed_image.astype(np.float32)
  preprocessed_image = preprocessed_image.transpose(2,0,1)
  #Reshaping the input array to align with the input shape of the model
  preprocessed_image = preprocessed_image.reshape(1,3,416,416)
  return preprocessed_image

def postprocess_output(out, frame, x_scale, y_scale, i):
  out = out[0][0]
  num_classes = 20
  anchors = [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]
  existing_labels = {l: [] for l in label}

  #Inside this loop we compute the bounding box b for grid cell (cy, cx)
  for cy in range(0,13):
    for cx in range(0,13):
      for b in range(0,5):
      # First we read the tx, ty, width(tw), and height(th) for the bounding box from the out array, as well as the confidence score
        channel = b*(num_classes+5)
        tx = out[channel  ][cy][cx]
        ty = out[channel+1][cy][cx]
        tw = out[channel+2][cy][cx]
        th = out[channel+3][cy][cx]
        tc = out[channel+4][cy][cx]

        x = (float(cx) + sigmoid(tx))*32
        y = (float(cy) + sigmoid(ty))*32
        w = np.exp(tw) * 32 * anchors[2*b]
        h = np.exp(th) * 32 * anchors[2*b+1] 

        #calculating the confidence score
        confidence = sigmoid(tc) # The confidence value for the bounding box is given by tc
        classes = np.zeros(num_classes)
        for c in range(0,num_classes):
          classes[c] = out[channel + 5 +c][cy][cx]
          # we take the softmax to turn the array into a probability distribution. And then we pick the class with the largest score as the winner.
          classes = softmax(classes)
          detected_class = classes.argmax()
          # Now we can compute the final score for this bounding box and we only want to keep the ones whose combined score is over a certain threshold
          if 0.60 < classes[detected_class]*confidence:
            color =clut[detected_class]
            x = (x - w/2)*x_scale
            y = (y - h/2)*y_scale
            w *= x_scale
            h *= y_scale
               
            labelX = int((x+x+w)/2)
            labelY = int((y+y+h)/2)
            addLabel = True
            lab_threshold = 100
            for point in existing_labels[label[detected_class]]:
              if labelX < point[0] + lab_threshold and labelX > point[0] - lab_threshold and \
                 labelY < point[1] + lab_threshold and labelY > point[1] - lab_threshold:
                  addLabel = False
              #Adding class labels to the output of the frame and also drawing a rectangular bounding box around the object detected.
            if addLabel:
              cv2.rectangle(frame, (int(x),int(y)),(int(x+w),int(y+h)),color,2)
              cv2.rectangle(frame, (int(x),int(y-13)),(int(x)+9*len(label[detected_class]),int(y)),color,-1)
              cv2.putText(frame,label[detected_class],(int(x)+2,int(y)-3),cv2.FONT_HERSHEY_COMPLEX,0.4,(255,255,255),1)
              existing_labels[label[detected_class]].append((labelX,labelY))
            print('{} detected in frame {}'.format(label[detected_class],i))
  

def show_bbox(device, frame, inference_time):
  cv2.putText(frame,device,(10,20),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1)
  cv2.putText(frame,'FPS: {}'.format(1.0/inference_time),(10,40),cv2.FONT_HERSHEY_COMPLEX,0.5,(255,255,255),1)
  frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
  cv2.imshow('frame',frame)

def main():
  
  # Process arguments
  args = parse_arguments()

  # Validate model file path
  check_model_extension(args.model)
  so = rt.SessionOptions()
  so.log_severity_level = 3
  if (args.device == 'cpu'):
    print("Device type selected is 'cpu' which is the default CPU Execution Provider (MLAS)")
    #Specify the path to the ONNX model on your machine and register the CPU EP
    sess = rt.InferenceSession(args.model, so, providers=['CPUExecutionProvider'])
  elif (args.device == 'CPU_FP32' or args.device == 'GPU_FP32' or args.device == 'GPU_FP16' or args.device == 'MYRIAD_FP16' or args.device == 'VADM_FP16'):
    #Specify the path to the ONNX model on your machine and register the OpenVINO EP
    sess = rt.InferenceSession(args.model, so, providers=['OpenVINOExecutionProvider'], provider_options=[{'device_type' : args.device}])
    print("Device type selected is: " + args.device + " using the OpenVINO Execution Provider")
    '''
    other 'device_type' options are: (Any hardware target can be assigned if you have the access to it)
    'CPU_FP32', 'GPU_FP32', 'GPU_FP16', 'MYRIAD_FP16', 'VAD-M_FP16'
    '''
  else:
    raise Exception("Device type selected is not [cpu, CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VADM_FP16]")

  # Get the input name of the model
  input_name = sess.get_inputs()[0].name

  #validate video file input path
  check_video_file_extension(args.video)

  #Path to video file has to be provided
  cap = cv2.VideoCapture(args.video)

  # capturing different metrics of the image from the video
  fps = cap.get(cv2.CAP_PROP_FPS)
  width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
  height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
  x_scale = float(width)/416.0  #In the document of tino-yolo-v2, input shape of this network is (1,3,416,416).
  y_scale = float(height)/416.0      
 
  # writing the inferencing output as a video to the local disk
  fourcc = cv2.VideoWriter_fourcc(*'XVID')
  output_video_name = args.device + "_output.avi"
  output_video = cv2.VideoWriter(output_video_name,fourcc, float(17.0), (640,360))

  # capturing one frame at a time from the video feed and performing the inference
  i = 0
  while cv2.waitKey(1) < 0:
    l_start = time.time()
    ret, frame = cap.read()
    if not ret:
      break
    initial_w = cap.get(3)
    initial_h = cap.get(4)
        
    # preprocessing the input frame and reshaping it.
    #In the document of tino-yolo-v2, input shape of this network is (1,3,416,416). so we resize the model frame w.r.t that size.
    preprocessed_image =  image_preprocess(frame)

    start = time.time()
    #Running the session by passing in the input data of the model
    out = sess.run(None, {input_name: preprocessed_image})
    end = time.time()
    inference_time = end - start

    #Get the output
    postprocess_output(out, frame, x_scale, y_scale, i)
   
    #Show the Output
    output_video.write(frame)
    show_bbox(args.device, frame, inference_time)
        
    #Press 'q' to quit the process
    print('Processed Frame {}'.format(i))
    i += 1
    l_end = time.time()
    print('Loop Time = {}'.format(l_end - l_start))

  output_video.release()
  cv2.destroyAllWindows()

if __name__ == "__main__":
  main()