addyolo

 code/web/package-lock.json
 code/web/package-lock.json
 code/web/node_modules/**
 code/web/node_modules/**
+/code/venv
+/code/deep_sort_yolov4/model_data/yolov4.weights
+/code/deep_sort_yolov4/model_data/yolo4_weight.h5
\ No newline at end of file

+# YOLOv4 + Deep_SORT
+
+<img src="https://github.com/yehengchen/Object-Detection-and-Tracking/blob/master/OneStage/yolo/deep_sort_yolov4/output/comparison.png" width="81%" height="81%"> <img src="https://github.com/yehengchen/video_demo/blob/master/video_demo/output.gif" width="40%" height="40%"> <img src="https://github.com/yehengchen/video_demo/blob/master/video_demo/TownCentreXVID_output.gif" width="40%" height="40%">
+
+__Object Tracking & Counting Demo - [[BiliBili]](https://www.bilibili.com/video/BV1Ug4y1i71w#reply3014975828)  [[Chinese Version]](https://blog.csdn.net/weixin_38107271/article/details/96741706)__
+## Requirement
+__Development Environment: [Deep-Learning-Environment-Setup](https://github.com/yehengchen/Ubuntu-16.04-Deep-Learning-Environment-Setup)__ 
+
+* OpenCV
+* sklean
+* pillow
+* numpy 1.15.0
+* torch 1.3.0
+* tensorflow-gpu 1.13.1
+* CUDA 10.0
+***
+
+It uses:
+
+* __Detection__: [YOLOv4](https://github.com/yehengchen/Object-Detection-and-Tracking/tree/master/OneStage/yolo/Train-a-YOLOv4-model) to detect objects on each of the video frames. - 用自己的数据训练YOLOv4模型
+
+* __Tracking__: [Deep_SORT](https://github.com/nwojke/deep_sort) to track those objects over different frames.
+
+*This repository contains code for Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT). We extend the original SORT algorithm to integrate appearance information based on a deep appearance descriptor. See the [arXiv preprint](https://arxiv.org/abs/1703.07402) for more information.*
+
+## Quick Start
+
+__0.Requirements__
+
+    pip install -r requirements.txt
+    
+__1. Download the code to your computer.__
+    
+    git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git
+    
+__2. Download [[yolov4.weights]](https://drive.google.com/file/d/1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT/view) [[Baidu]](https://pan.baidu.com/s/1jRudrrXAS3DRGqT6mL4L3A ) - `mnv6`__ and place it in `deep_sort_yolov4/model_data/`
+
+*Here you can download my trained [[yolo4_weight.h5]](https://pan.baidu.com/s/1JuT4KCUFaE2Gvme0_S37DQ ) - `w17w` weights for detecting person/car/bicycle,etc.*
+
+__3. Convert the Darknet YOLO model to a Keras model:__
+```
+$ python convert.py model_data/yolov4.cfg model_data/yolov4.weights model_data/yolo.h5
+``` 
+__4. Run the YOLO_DEEP_SORT:__
+
+```
+$ python main.py -c [CLASS NAME] -i [INPUT VIDEO PATH]
+
+$ python main.py -c person -i ./test_video/testvideo.avi
+```
+
+__5. Can change [deep_sort_yolov3/yolo.py] `__Line 100__` to your tracking object__
+
+*DeepSORT pre-trained weights using people-ReID datasets only for person*
+```
+    if predicted_class != args["class"]:
+               continue
+    
+    if predicted_class != 'person' and predicted_class != 'car':
+               continue
+```
+
+## Train on Market1501 & MARS
+*People Re-identification model*
+
+[cosine_metric_learning](https://github.com/nwojke/cosine_metric_learning) for training a metric feature representation to be used with the deep_sort tracker.
+
+## Citation
+
+### YOLOv4 :
+
+    @misc{bochkovskiy2020yolov4,
+    title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
+    author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
+    year={2020},
+    eprint={2004.10934},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+    }
+
+### Deep_SORT :
+
+    @inproceedings{Wojke2017simple,
+    title={Simple Online and Realtime Tracking with a Deep Association Metric},
+    author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
+    booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
+    year={2017},
+    pages={3645--3649},
+    organization={IEEE},
+    doi={10.1109/ICIP.2017.8296962}
+    }
+
+    @inproceedings{Wojke2018deep,
+    title={Deep Cosine Metric Learning for Person Re-identification},
+    author={Wojke, Nicolai and Bewley, Alex},
+    booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
+    year={2018},
+    pages={748--756},
+    organization={IEEE},
+    doi={10.1109/WACV.2018.00087}
+    }
+    
+## Reference
+#### Github:deep_sort@[Nicolai Wojke nwojke](https://github.com/nwojke/deep_sort)
+#### Github:deep_sort_yolov3@[Qidian213 ](https://github.com/Qidian213/deep_sort_yolov3)
+#### Github:Deep-SORT-YOLOv4@[LeonLok](https://github.com/LeonLok/Deep-SORT-YOLOv4)
+

+import os
+import colorsys
+
+import numpy as np
+from keras import backend as K
+from keras.models import load_model
+from keras.layers import Input
+
+from yolo4.model import yolo_eval, yolo4_body
+from yolo4.utils import letterbox_image
+
+from PIL import Image, ImageFont, ImageDraw
+from timeit import default_timer as timer
+import matplotlib.pyplot as plt
+
+from operator import itemgetter
+
+class Yolo4(object):
+    def get_class(self):
+        classes_path = os.path.expanduser(self.classes_path)
+        with open(classes_path) as f:
+            class_names = f.readlines()
+        class_names = [c.strip() for c in class_names]
+        return class_names
+
+    def get_anchors(self):
+        anchors_path = os.path.expanduser(self.anchors_path)
+        with open(anchors_path) as f:
+            anchors = f.readline()
+        anchors = [float(x) for x in anchors.split(',')]
+        return np.array(anchors).reshape(-1, 2)
+
+    def load_yolo(self):
+        model_path = os.path.expanduser(self.model_path)
+        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
+
+        self.class_names = self.get_class()
+        self.anchors = self.get_anchors()
+
+        num_anchors = len(self.anchors)
+        num_classes = len(self.class_names)
+
+        # Generate colors for drawing bounding boxes.
+        hsv_tuples = [(x / len(self.class_names), 1., 1.)
+                      for x in range(len(self.class_names))]
+        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+        self.colors = list(
+            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
+                self.colors))
+
+        self.sess = K.get_session()
+
+        # Load model, or construct model and load weights.
+        self.yolo4_model = yolo4_body(Input(shape=(608, 608, 3)), num_anchors//3, num_classes)
+
+        # Read and convert darknet weight
+        print('Loading weights.')
+        weights_file = open(self.weights_path, 'rb')
+        major, minor, revision = np.ndarray(
+            shape=(3, ), dtype='int32', buffer=weights_file.read(12))
+        if (major*10+minor)>=2 and major<1000 and minor<1000:
+            seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))
+        else:
+            seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))
+        print('Weights Header: ', major, minor, revision, seen)
+
+        convs_to_load = []
+        bns_to_load = []
+        for i in range(len(self.yolo4_model.layers)):
+            layer_name = self.yolo4_model.layers[i].name
+            if layer_name.startswith('conv2d_'):
+                convs_to_load.append((int(layer_name[7:]), i))
+            if layer_name.startswith('batch_normalization_'):
+                bns_to_load.append((int(layer_name[20:]), i))
+
+        convs_sorted = sorted(convs_to_load, key=itemgetter(0))
+        bns_sorted = sorted(bns_to_load, key=itemgetter(0))
+
+        bn_index = 0
+        for i in range(len(convs_sorted)):
+            print('Converting ', i)
+            if i == 93 or i == 101 or i == 109:
+                #no bn, with bias
+                weights_shape = self.yolo4_model.layers[convs_sorted[i][1]].get_weights()[0].shape
+                bias_shape = self.yolo4_model.layers[convs_sorted[i][1]].get_weights()[0].shape[3]
+                filters = bias_shape
+                size = weights_shape[0]
+                darknet_w_shape = (filters, weights_shape[2], size, size)
+                weights_size = np.product(weights_shape)
+
+                conv_bias = np.ndarray(
+                    shape=(filters, ),
+                    dtype='float32',
+                    buffer=weights_file.read(filters * 4))
+                conv_weights = np.ndarray(
+                    shape=darknet_w_shape,
+                    dtype='float32',
+                    buffer=weights_file.read(weights_size * 4))
+                conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
+                self.yolo4_model.layers[convs_sorted[i][1]].set_weights([conv_weights, conv_bias])
+            else:
+                #with bn, no bias
+                weights_shape = self.yolo4_model.layers[convs_sorted[i][1]].get_weights()[0].shape
+                size = weights_shape[0]
+                bn_shape = self.yolo4_model.layers[bns_sorted[bn_index][1]].get_weights()[0].shape
+                filters = bn_shape[0]
+                darknet_w_shape = (filters, weights_shape[2], size, size)
+                weights_size = np.product(weights_shape)
+
+                conv_bias = np.ndarray(
+                    shape=(filters, ),
+                    dtype='float32',
+                    buffer=weights_file.read(filters * 4))
+                bn_weights = np.ndarray(
+                    shape=(3, filters),
+                    dtype='float32',
+                    buffer=weights_file.read(filters * 12))
+
+                bn_weight_list = [
+                    bn_weights[0],  # scale gamma
+                    conv_bias,  # shift beta
+                    bn_weights[1],  # running mean
+                    bn_weights[2]  # running var
+                ]
+                self.yolo4_model.layers[bns_sorted[bn_index][1]].set_weights(bn_weight_list)
+
+                conv_weights = np.ndarray(
+                    shape=darknet_w_shape,
+                    dtype='float32',
+                    buffer=weights_file.read(weights_size * 4))
+                conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
+                self.yolo4_model.layers[convs_sorted[i][1]].set_weights([conv_weights])
+
+                bn_index += 1
+
+        weights_file.close()
+
+        self.yolo4_model.save(self.model_path)
+
+
+        if self.gpu_num>=2:
+            self.yolo4_model = multi_gpu_model(self.yolo4_model, gpus=self.gpu_num)
+
+        self.input_image_shape = K.placeholder(shape=(2, ))
+        self.boxes, self.scores, self.classes = yolo_eval(self.yolo4_model.output, self.anchors,
+                len(self.class_names), self.input_image_shape,
+                score_threshold=self.score)
+
+    def __init__(self, score, iou, anchors_path, classes_path, model_path, weights_path, gpu_num=1):
+        self.score = score
+        self.iou = iou
+        self.anchors_path = anchors_path
+        self.classes_path = classes_path
+        self.weights_path = weights_path
+        self.model_path = model_path
+        self.gpu_num = gpu_num
+        self.load_yolo()
+
+    def close_session(self):
+        self.sess.close()
+
+if __name__ == '__main__':
+    model_path = 'model_data/yolo4_weight.h5'
+    anchors_path = 'model_data/yolo_anchors.txt'
+    classes_path = 'model_data/coco_classes.txt'
+    weights_path = 'model_data/yolov4.weights'
+
+    score = 0.5
+    iou = 0.5
+
+    model_image_size = (608, 608)
+
+    yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path)
+
+    yolo4_model.close_session()
\ No newline at end of file

+# vim: expandtab:ts=4:sw=4

+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection(object):
+    """
+    This class represents a bounding box detection in a single image.
+
+    Parameters
+    ----------
+    tlwh : array_like
+        Bounding box in format `(x, y, w, h)`.
+    confidence : float
+        Detector confidence score.
+    feature : array_like
+        A feature vector that describes the object contained in this image.
+
+    Attributes
+    ----------
+    tlwh : ndarray
+        Bounding box in format `(top left x, top left y, width, height)`.
+    confidence : ndarray
+        Detector confidence score.
+    feature : ndarray | NoneType
+        A feature vector that describes the object contained in this image.
+
+    """
+
+    def __init__(self, tlwh, confidence, feature):
+        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.confidence = float(confidence)
+        self.feature = np.asarray(feature, dtype=np.float32)
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret

+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection_YOLO(object):
+    """
+    This class represents a bounding box detection in a single image.
+    Parameters
+    ----------
+    tlwh : array_like
+        Bounding box in format `(x, y, w, h)`.
+    confidence : float
+        Detector confidence score.
+    feature : array_like
+        A feature vector that describes the object contained in this image.
+    Attributesutils
+    ----------
+    tlwh : ndarray
+        Bounding box in format `(top left x, top left y, width, height)`.
+    confidence : ndarray
+        Detector confidence score.
+    feature : ndarray | NoneType
+        A feature vector that describes the object contained in this image.
+    """
+
+    def __init__(self, tlwh, confidence, cls):
+        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.confidence = float(confidence)
+        self.cls = cls
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret

+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import linear_assignment
+
+
+def iou(bbox, candidates):
+    """Computer intersection over union.
+
+    Parameters
+    ----------
+    bbox : ndarray
+        A bounding box in format `(top left x, top left y, width, height)`.
+    candidates : ndarray
+        A matrix of candidate bounding boxes (one per row) in the same format
+        as `bbox`.
+
+    Returns
+    -------
+    ndarray
+        The intersection over union in [0, 1] between the `bbox` and each
+        candidate. A higher score means a larger fraction of the `bbox` is
+        occluded by the candidate.
+
+    """
+    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
+    candidates_tl = candidates[:, :2]
+    candidates_br = candidates[:, :2] + candidates[:, 2:]
+
+    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
+               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
+    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
+               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
+    wh = np.maximum(0., br - tl)
+
+    area_intersection = wh.prod(axis=1)
+    area_bbox = bbox[2:].prod()
+    area_candidates = candidates[:, 2:].prod(axis=1)
+    return area_intersection / (area_bbox + area_candidates - area_intersection)
+
+
+def iou_cost(tracks, detections, track_indices=None,
+             detection_indices=None):
+    """An intersection over union distance metric.
+
+    Parameters
+    ----------
+    tracks : List[deep_sort.track.Track]
+        A list of tracks.
+    detections : List[deep_sort.detection.Detection]
+        A list of detections.
+    track_indices : Optional[List[int]]
+        A list of indices to tracks that should be matched. Defaults to
+        all `tracks`.
+    detection_indices : Optional[List[int]]
+        A list of indices to detections that should be matched. Defaults
+        to all `detections`.
+
+    Returns
+    -------
+    ndarray
+        Returns a cost matrix of shape
+        len(track_indices), len(detection_indices) where entry (i, j) is
+        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
+    for row, track_idx in enumerate(track_indices):
+        if tracks[track_idx].time_since_update > 1:
+            cost_matrix[row, :] = linear_assignment.INFTY_COST
+            continue
+
+        bbox = tracks[track_idx].to_tlwh()
+        candidates = np.asarray([detections[i].tlwh for i in detection_indices])
+        cost_matrix[row, :] = 1. - iou(bbox, candidates)
+    return cost_matrix

+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, a, h, vx, vy, va, vh
+
+    contains the bounding box center position (x, y), aspect ratio a, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, a, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, a, h) with center position (x, y),
+            aspect ratio a, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(self._motion_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
+            is the center position, a the aspect ratio, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False):
+        """Compute gating distance between state distribution and measurements.
+
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        cholesky_factor = np.linalg.cholesky(covariance)
+        d = measurements - mean
+        z = scipy.linalg.solve_triangular(
+            cholesky_factor, d.T, lower=True, check_finite=False,
+            overwrite_b=True)
+        squared_maha = np.sum(z * z, axis=0)
+        return squared_maha

+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from sklearn.utils.linear_assignment_ import linear_assignment
+from . import kalman_filter
+
+
+INFTY_COST = 1e+5
+
+
+def min_cost_matching(
+        distance_metric, max_distance, tracks, detections, track_indices=None,
+        detection_indices=None):
+    """Solve linear assignment problem.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection_indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    if len(detection_indices) == 0 or len(track_indices) == 0:
+        return [], track_indices, detection_indices  # Nothing to match.
+
+    cost_matrix = distance_metric(
+        tracks, detections, track_indices, detection_indices)
+    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
+    indices = linear_assignment(cost_matrix)
+
+    matches, unmatched_tracks, unmatched_detections = [], [], []
+    for col, detection_idx in enumerate(detection_indices):
+        if col not in indices[:, 1]:
+            unmatched_detections.append(detection_idx)
+    for row, track_idx in enumerate(track_indices):
+        if row not in indices[:, 0]:
+            unmatched_tracks.append(track_idx)
+    for row, col in indices:
+        track_idx = track_indices[row]
+        detection_idx = detection_indices[col]
+        if cost_matrix[row, col] > max_distance:
+            unmatched_tracks.append(track_idx)
+            unmatched_detections.append(detection_idx)
+        else:
+            matches.append((track_idx, detection_idx))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def matching_cascade(
+        distance_metric, max_distance, cascade_depth, tracks, detections,
+        track_indices=None, detection_indices=None):
+    """Run matching cascade.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    cascade_depth: int
+        The cascade depth, should be se to the maximum track age.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : Optional[List[int]]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above). Defaults to all tracks.
+    detection_indices : Optional[List[int]]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above). Defaults to all
+        detections.
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = list(range(len(tracks)))
+    if detection_indices is None:
+        detection_indices = list(range(len(detections)))
+
+    unmatched_detections = detection_indices
+    matches = []
+    for level in range(cascade_depth):
+        if len(unmatched_detections) == 0:  # No detections left
+            break
+
+        track_indices_l = [
+            k for k in track_indices
+            if tracks[k].time_since_update == 1 + level
+        ]
+        if len(track_indices_l) == 0:  # Nothing to match at this level
+            continue
+
+        matches_l, _, unmatched_detections = \
+            min_cost_matching(
+                distance_metric, max_distance, tracks, detections,
+                track_indices_l, unmatched_detections)
+        matches += matches_l
+    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def gate_cost_matrix(
+        kf, cost_matrix, tracks, detections, track_indices, detection_indices,
+        gated_cost=INFTY_COST, only_position=False):
+    """Invalidate infeasible entries in cost matrix based on the state
+    distributions obtained by Kalman filtering.
+
+    Parameters
+    ----------
+    kf : The Kalman filter.
+    cost_matrix : ndarray
+        The NxM dimensional cost matrix, where N is the number of track indices
+        and M is the number of detection indices, such that entry (i, j) is the
+        association cost between `tracks[track_indices[i]]` and
+        `detections[detection_indices[j]]`.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+    gated_cost : Optional[float]
+        Entries in the cost matrix corresponding to infeasible associations are
+        set this value. Defaults to a very large value.
+    only_position : Optional[bool]
+        If True, only the x, y position of the state distribution is considered
+        during gating. Defaults to False.
+
+    Returns
+    -------
+    ndarray
+        Returns the modified cost matrix.
+
+    """
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray(
+        [detections[i].to_xyah() for i in detection_indices])
+    for row, track_idx in enumerate(track_indices):
+        track = tracks[track_idx]
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position)
+        cost_matrix[row, gating_distance > gating_threshold] = gated_cost
+    return cost_matrix

+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+def _pdist(a, b):
+    """Compute pair-wise squared distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    a, b = np.asarray(a), np.asarray(b)
+    if len(a) == 0 or len(b) == 0:
+        return np.zeros((len(a), len(b)))
+    a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
+    r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
+    r2 = np.clip(r2, 0., float(np.inf))
+    return r2
+
+
+def _cosine_distance(a, b, data_is_normalized=False):
+    """Compute pair-wise cosine distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+    data_is_normalized : Optional[bool]
+        If True, assumes rows in a and b are unit length vectors.
+        Otherwise, a and b are explicitly normalized to lenght 1.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    if not data_is_normalized:
+        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
+        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+    return 1. - np.dot(a, b.T)
+
+
+def _nn_euclidean_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (Euclidean).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest Euclidean distance to a sample in `x`.
+
+    """
+    distances = _pdist(x, y)
+    return np.maximum(0.0, distances.min(axis=0))
+
+
+def _nn_cosine_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (cosine).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest cosine distance to a sample in `x`.
+
+    """
+    distances = _cosine_distance(x, y)
+    return distances.min(axis=0)
+
+
+class NearestNeighborDistanceMetric(object):
+    """
+    A nearest neighbor distance metric that, for each target, returns
+    the closest distance to any sample that has been observed so far.
+
+    Parameters
+    ----------
+    metric : str
+        Either "euclidean" or "cosine".
+    matching_threshold: float
+        The matching threshold. Samples with larger distance are considered an
+        invalid match.
+    budget : Optional[int]
+        If not None, fix samples per class to at most this number. Removes
+        the oldest samples when the budget is reached.
+
+    Attributes
+    ----------
+    samples : Dict[int -> List[ndarray]]
+        A dictionary that maps from target identities to the list of samples
+        that have been observed so far.
+
+    """
+
+    def __init__(self, metric, matching_threshold, budget=None):
+
+
+        if metric == "euclidean":
+            self._metric = _nn_euclidean_distance
+        elif metric == "cosine":
+            self._metric = _nn_cosine_distance
+        else:
+            raise ValueError(
+                "Invalid metric; must be either 'euclidean' or 'cosine'")
+        self.matching_threshold = matching_threshold
+        self.budget = budget
+        self.samples = {}
+
+    def partial_fit(self, features, targets, active_targets):
+        """Update the distance metric with new data.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : ndarray
+            An integer array of associated target identities.
+        active_targets : List[int]
+            A list of targets that are currently present in the scene.
+
+        """
+        for feature, target in zip(features, targets):
+            self.samples.setdefault(target, []).append(feature)
+            if self.budget is not None:
+                self.samples[target] = self.samples[target][-self.budget:]
+        self.samples = {k: self.samples[k] for k in active_targets}
+
+    def distance(self, features, targets):
+        """Compute distance between features and targets.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : List[int]
+            A list of targets to match the given `features` against.
+
+        Returns
+        -------
+        ndarray
+            Returns a cost matrix of shape len(targets), len(features), where
+            element (i, j) contains the closest squared distance between
+            `targets[i]` and `features[j]`.
+
+        """
+        cost_matrix = np.zeros((len(targets), len(features)))
+        for i, target in enumerate(targets):
+            cost_matrix[i, :] = self._metric(self.samples[target], features)
+        return cost_matrix

+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import cv2
+
+
+def non_max_suppression(boxes, max_bbox_overlap, scores=None):
+    """Suppress overlapping detections.
+
+    Original code from [1]_ has been adapted to include confidence score.
+
+    .. [1] http://www.pyimagesearch.com/2015/02/16/
+           faster-non-maximum-suppression-python/
+
+    Examples
+    --------
+
+        >>> boxes = [d.roi for d in detections]
+        >>> scores = [d.confidence for d in detections]
+        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
+        >>> detections = [detections[i] for i in indices]
+
+    Parameters
+    ----------
+    boxes : ndarray
+        Array of ROIs (x, y, width, height).
+    max_bbox_overlap : float
+        ROIs that overlap more than this values are suppressed.
+    scores : Optional[array_like]
+        Detector confidence score.
+
+    Returns
+    -------
+    List[int]
+        Returns indices of detections that have survived non-maxima suppression.
+
+    """
+    if len(boxes) == 0:
+        return []
+
+    boxes = boxes.astype(np.float)
+    pick = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2] + boxes[:, 0]
+    y2 = boxes[:, 3] + boxes[:, 1]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    if scores is not None:
+        idxs = np.argsort(scores)
+    else:
+        idxs = np.argsort(y2)
+
+    while len(idxs) > 0:
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        overlap = (w * h) / area[idxs[:last]]
+
+        idxs = np.delete(
+            idxs, np.concatenate(
+                ([last], np.where(overlap > max_bbox_overlap)[0])))
+
+    return pick

+# vim: expandtab:ts=4:sw=4
+
+
+class TrackState:
+    """
+    Enumeration type for the single target track state. Newly created tracks are
+    classified as `tentative` until enough evidence has been collected. Then,
+    the track state is changed to `confirmed`. Tracks that are no longer alive
+    are classified as `deleted` to mark them for removal from the set of active
+    tracks.
+
+    """
+
+    Tentative = 1
+    Confirmed = 2
+    Deleted = 3
+
+
+class Track:
+    """
+    A single target track with state space `(x, y, a, h)` and associated
+    velocities, where `(x, y)` is the center of the bounding box, `a` is the
+    aspect ratio and `h` is the height.
+
+    Parameters
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+    max_age : int
+        The maximum number of consecutive misses before the track state is
+        set to `Deleted`.
+    feature : Optional[ndarray]
+        Feature vector of the detection this track originates from. If not None,
+        this feature is added to the `features` cache.
+
+    Attributes
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    hits : int
+        Total number of measurement updates.
+    age : int
+        Total number of frames since first occurance.
+    time_since_update : int
+        Total number of frames since last measurement update.
+    state : TrackState
+        The current track state.
+    features : List[ndarray]
+        A cache of features. On each measurement update, the associated feature
+        vector is added to this list.
+
+    """
+
+    def __init__(self, mean, covariance, track_id, n_init, max_age,
+                 feature=None):
+        self.mean = mean
+        self.covariance = covariance
+        self.track_id = track_id
+        self.hits = 1
+        self.age = 1
+        self.time_since_update = 0
+
+        self.state = TrackState.Tentative
+        self.features = []
+        if feature is not None:
+            self.features.append(feature)
+
+        self._n_init = n_init
+        self._max_age = max_age
+
+    def to_tlwh(self):
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    def to_tlbr(self):
+        """Get current position in bounding box format `(min x, miny, max x,
+        max y)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.to_tlwh()
+        ret[2:] = ret[:2] + ret[2:]
+        return ret
+
+    def predict(self, kf):
+        """Propagate the state distribution to the current time step using a
+        Kalman filter prediction step.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+
+        """
+        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
+        self.age += 1
+        self.time_since_update += 1
+
+    def update(self, kf, detection):
+        """Perform Kalman filter measurement update step and update the feature
+        cache.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+        detection : Detection
+            The associated detection.
+
+        """
+        self.mean, self.covariance = kf.update(
+            self.mean, self.covariance, detection.to_xyah())
+        self.features.append(detection.feature)
+
+        self.hits += 1
+        self.time_since_update = 0
+        if self.state == TrackState.Tentative and self.hits >= self._n_init:
+            self.state = TrackState.Confirmed
+
+    def mark_missed(self):
+        """Mark this track as missed (no association at the current time step).
+        """
+        if self.state == TrackState.Tentative:
+            self.state = TrackState.Deleted
+        elif self.time_since_update > self._max_age:
+            self.state = TrackState.Deleted
+
+    def is_tentative(self):
+        """Returns True if this track is tentative (unconfirmed).
+        """
+        return self.state == TrackState.Tentative
+
+    def is_confirmed(self):
+        """Returns True if this track is confirmed."""
+        return self.state == TrackState.Confirmed
+
+    def is_deleted(self):
+        """Returns True if this track is dead and should be deleted."""
+        return self.state == TrackState.Deleted

+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import kalman_filter
+from . import linear_assignment
+from . import iou_matching
+from .track import Track
+
+
+class Tracker:
+    """
+    This is the multi-target tracker.
+
+    Parameters
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        A distance metric for measurement-to-track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+
+    Attributes
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        The distance metric used for measurement to track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of frames that a track remains in initialization phase.
+    kf : kalman_filter.KalmanFilter
+        A Kalman filter to filter target trajectories in image space.
+    tracks : List[Track]
+        The list of active tracks at the current time step.
+
+    """
+
+    def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3):
+        self.metric = metric
+        self.max_iou_distance = max_iou_distance
+        self.max_age = max_age
+        self.n_init = n_init
+
+        self.kf = kalman_filter.KalmanFilter()
+        self.tracks = []
+        self._next_id = 1
+
+    def predict(self):
+        """Propagate track state distributions one time step forward.
+
+        This function should be called once every time step, before `update`.
+        """
+        for track in self.tracks:
+            track.predict(self.kf)
+
+    def update(self, detections):
+        """Perform measurement update and track management.
+
+        Parameters
+        ----------
+        detections : List[deep_sort.detection.Detection]
+            A list of detections at the current time step.
+
+        """
+        # Run matching cascade.
+        matches, unmatched_tracks, unmatched_detections = \
+            self._match(detections)
+
+        # Update track set.
+        for track_idx, detection_idx in matches:
+            self.tracks[track_idx].update(
+                self.kf, detections[detection_idx])
+        for track_idx in unmatched_tracks:
+            self.tracks[track_idx].mark_missed()
+        for detection_idx in unmatched_detections:
+            self._initiate_track(detections[detection_idx])
+        self.tracks = [t for t in self.tracks if not t.is_deleted()]
+
+        # Update distance metric.
+        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
+        features, targets = [], []
+        for track in self.tracks:
+            if not track.is_confirmed():
+                continue
+            features += track.features
+            targets += [track.track_id for _ in track.features]
+            track.features = []
+        self.metric.partial_fit(
+            np.asarray(features), np.asarray(targets), active_targets)
+
+    def _match(self, detections):
+
+        def gated_metric(tracks, dets, track_indices, detection_indices):
+            features = np.array([dets[i].feature for i in detection_indices])
+            targets = np.array([tracks[i].track_id for i in track_indices])
+            cost_matrix = self.metric.distance(features, targets)
+            cost_matrix = linear_assignment.gate_cost_matrix(
+                self.kf, cost_matrix, tracks, dets, track_indices,
+                detection_indices)
+
+            return cost_matrix
+
+        # Split track set into confirmed and unconfirmed tracks.
+        confirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if t.is_confirmed()]
+        unconfirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
+
+        # Associate confirmed tracks using appearance features.
+        matches_a, unmatched_tracks_a, unmatched_detections = \
+            linear_assignment.matching_cascade(
+                gated_metric, self.metric.matching_threshold, self.max_age,
+                self.tracks, detections, confirmed_tracks)
+
+        # Associate remaining tracks together with unconfirmed tracks using IOU.
+        iou_track_candidates = unconfirmed_tracks + [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update == 1]
+        unmatched_tracks_a = [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update != 1]
+        matches_b, unmatched_tracks_b, unmatched_detections = \
+            linear_assignment.min_cost_matching(
+                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
+                detections, iou_track_candidates, unmatched_detections)
+
+        matches = matches_a + matches_b
+        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
+        return matches, unmatched_tracks, unmatched_detections
+
+    def _initiate_track(self, detection):
+        mean, covariance = self.kf.initiate(detection.to_xyah())
+        self.tracks.append(Track(
+            mean, covariance, self._next_id, self.n_init, self.max_age,
+            detection.feature))
+        self._next_id += 1

+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

+person

+12, 16,  19, 36,  40, 28,  36, 75,  76, 55,  72, 146,  142, 110,  192, 243,  459, 401

+Output_video
+

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\ No newline at end of file

+{"schema":{"fields":[{"name":"index","type":"string"},{"name":"total","type":"string"},{"name":"now","type":"string"},{"name":"time","type":"string"},{"name":"s","type":"string"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[]}
\ No newline at end of file

+{"schema":{"fields":[{"name":"index","type":"string"},{"name":"x","type":"string"},{"name":"y","type":"string"},{"name":"id","type":"string"},{"name":"time","type":"string"},{"name":"s","type":"string"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[]}
\ No newline at end of file

+Keras==2.3.1
+tensorflow-gpu==1.13.1
+numpy==1.15.0
+opencv-python==4.2.0.34
+scikit-learn==0.23.1
+scipy==1.4.1
+Pillow
+torch==1.4.0
+torchvision==0.5.0

+Intput_video
+

+import cv2
+import os
+import moviepy.editor as mp
+
+video = "00046"
+#input_imgs
+im_dir = '/home/cai/Desktop/yolo_dataset/t1_video/t1_video_'+video+'/'
+#im_dir =
+#output_video
+video_dir = '/home/cai/Desktop/yolo_dataset/t1_video/test_video/det_t1_video_'+video+'_test_q.avi'
+#fps
+fps = 50
+#num_of_imgs
+num = 310
+#img_size
+img_size = (1920,1080)
+fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
+#opencv3
+
+videoWriter = cv2.VideoWriter(video_dir, fourcc, fps, img_size)
+for i in range(0,num):
+    #im_name = os.path.join(im_dir,'frame-' + str(i) + '.png')
+    im_name = os.path.join(im_dir,'t1_video_'+video+'_' + "%05d" % i + '.jpg')
+    frame = cv2.imread(im_name)
+    #frame = cv2.resize(frame, (480, 320))
+    #frame = cv2.resize(frame,(520,320), interpolation=cv2.INTER_CUBIC)
+    videoWriter.write(frame)
+    print (im_name)
+videoWriter.release()
+print('finish')

+import os
+import errno
+import argparse
+import numpy as np
+import cv2
+import tensorflow as tf
+
+
+def _run_in_batches(f, data_dict, out, batch_size):
+    data_len = len(out)
+    num_batches = int(data_len / batch_size)
+
+    s, e = 0, 0
+    for i in range(num_batches):
+        s, e = i * batch_size, (i + 1) * batch_size
+        batch_data_dict = {k: v[s:e] for k, v in data_dict.items()}
+        out[s:e] = f(batch_data_dict)
+    if e < len(out):
+        batch_data_dict = {k: v[e:] for k, v in data_dict.items()}
+        out[e:] = f(batch_data_dict)
+
+
+def extract_image_patch(image, bbox, patch_shape):
+    """Extract image patch from bounding box.
+    Parameters
+    ----------
+    image : ndarray
+        The full image.
+    bbox : array_like
+        The bounding box in format (x, y, width, height).
+    patch_shape : Optional[array_like]
+        This parameter can be used to enforce a desired patch shape
+        (height, width). First, the `bbox` is adapted to the aspect ratio
+        of the patch shape, then it is clipped at the image boundaries.
+        If None, the shape is computed from :arg:`bbox`.
+    Returns
+    -------
+    ndarray | NoneType
+        An image patch showing the :arg:`bbox`, optionally reshaped to
+        :arg:`patch_shape`.
+        Returns None if the bounding box is empty or fully outside of the image
+        boundaries.
+    """
+    bbox = np.array(bbox)
+    if patch_shape is not None:
+        # correct aspect ratio to patch shape
+        target_aspect = float(patch_shape[1]) / patch_shape[0]
+        new_width = target_aspect * bbox[3]
+        bbox[0] -= (new_width - bbox[2]) / 2
+        bbox[2] = new_width
+
+    # convert to top left, bottom right
+    bbox[2:] += bbox[:2]
+    bbox = bbox.astype(np.int)
+
+    # clip at image boundaries
+    bbox[:2] = np.maximum(0, bbox[:2])
+    bbox[2:] = np.minimum(np.asarray(image.shape[:2][::-1]) - 1, bbox[2:])
+    if np.any(bbox[:2] >= bbox[2:]):
+        return None
+    sx, sy, ex, ey = bbox
+    image = image[sy:ey, sx:ex]
+    image = cv2.resize(image, tuple(patch_shape[::-1]))
+    return image
+
+
+class ImageEncoder(object):
+
+    def __init__(self, checkpoint_filename, input_name="images",
+                 output_name="features"):
+        self.session = tf.Session()
+        with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(file_handle.read())
+        tf.import_graph_def(graph_def, name="net")
+        self.input_var = tf.get_default_graph().get_tensor_by_name(
+            "net/%s:0" % input_name)
+        self.output_var = tf.get_default_graph().get_tensor_by_name(
+            "net/%s:0" % output_name)
+
+        assert len(self.output_var.get_shape()) == 2
+        assert len(self.input_var.get_shape()) == 4
+        self.feature_dim = self.output_var.get_shape().as_list()[-1]
+        self.image_shape = self.input_var.get_shape().as_list()[1:]
+
+    def __call__(self, data_x, batch_size=32):
+        out = np.zeros((len(data_x), self.feature_dim), np.float32)
+        _run_in_batches(
+            lambda x: self.session.run(self.output_var, feed_dict=x),
+            {self.input_var: data_x}, out, batch_size)
+        return out
+
+
+def create_box_encoder(model_filename, input_name="images",
+                       output_name="features", batch_size=32):
+    image_encoder = ImageEncoder(model_filename, input_name, output_name)
+    image_shape = image_encoder.image_shape
+
+    def encoder(image, boxes):
+        image_patches = []
+        for box in boxes:
+            patch = extract_image_patch(image, box, image_shape[:2])
+            if patch is None:
+                print("WARNING: Failed to extract image patch: %s." % str(box))
+                patch = np.random.uniform(
+                    0., 255., image_shape).astype(np.uint8)
+            image_patches.append(patch)
+        image_patches = np.asarray(image_patches)
+        return image_encoder(image_patches, batch_size)
+
+    return encoder
+
+
+def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
+    """Generate detections with features.
+    Parameters
+    ----------
+    encoder : Callable[image, ndarray] -> ndarray
+        The encoder function takes as input a BGR color image and a matrix of
+        bounding boxes in format `(x, y, w, h)` and returns a matrix of
+        corresponding feature vectors.
+    mot_dir : str
+        Path to the MOTChallenge directory (can be either train or test).
+    output_dir
+        Path to the output directory. Will be created if it does not exist.
+    detection_dir
+        Path to custom detections. The directory structure should be the default
+        MOTChallenge structure: `[sequence]/det/det.txt`. If None, uses the
+        standard MOTChallenge detections.
+    """
+    if detection_dir is None:
+        detection_dir = mot_dir
+    try:
+        os.makedirs(output_dir)
+    except OSError as exception:
+        if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
+            pass
+        else:
+            raise ValueError(
+                "Failed to created output directory '%s'" % output_dir)
+
+    for sequence in os.listdir(mot_dir):
+        print("Processing %s" % sequence)
+        sequence_dir = os.path.join(mot_dir, sequence)
+
+        image_dir = os.path.join(sequence_dir, "img1")
+        image_filenames = {
+            int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
+            for f in os.listdir(image_dir)}
+
+        detection_file = os.path.join(
+            detection_dir, sequence, "det/det.txt")
+        detections_in = np.loadtxt(detection_file, delimiter=',')
+        detections_out = []
+
+        frame_indices = detections_in[:, 0].astype(np.int)
+        min_frame_idx = frame_indices.astype(np.int).min()
+        max_frame_idx = frame_indices.astype(np.int).max()
+        for frame_idx in range(min_frame_idx, max_frame_idx + 1):
+            print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
+            mask = frame_indices == frame_idx
+            rows = detections_in[mask]
+
+            if frame_idx not in image_filenames:
+                print("WARNING could not find image for frame %d" % frame_idx)
+                continue
+            bgr_image = cv2.imread(
+                image_filenames[frame_idx], cv2.IMREAD_COLOR)
+            features = encoder(bgr_image, rows[:, 2:6].copy())
+            detections_out += [np.r_[(row, feature)] for row, feature
+                               in zip(rows, features)]
+
+        output_filename = os.path.join(output_dir, "%s.npy" % sequence)
+        np.save(
+            output_filename, np.asarray(detections_out), allow_pickle=False)
+
+
+def parse_args():
+    """Parse command line arguments.
+    """
+    parser = argparse.ArgumentParser(description="Re-ID feature extractor")
+    parser.add_argument(
+        "--model",
+        default="resources/networks/mars-small128.pb",
+        help="Path to freezed inference graph protobuf.")
+    parser.add_argument(
+        "--mot_dir", help="Path to MOTChallenge directory (train or test)",
+        required=True)
+    parser.add_argument(
+        "--detection_dir", help="Path to custom detections. Defaults to "
+        "standard MOT detections Directory structure should be the default "
+        "MOTChallenge structure: [sequence]/det/det.txt", default=None)
+    parser.add_argument(
+        "--output_dir", help="Output directory. Will be created if it does not"
+        " exist.", default="detections")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    encoder = create_box_encoder(args.model, batch_size=32)
+    generate_detections(encoder, args.mot_dir, args.output_dir,
+                        args.detection_dir)
+
+
+if __name__ == "__main__":
+    main()

+import cv2
+
+image_folder = './mask_face'
+video_name = './cut_test.m4v'
+
+vc = cv2.VideoCapture(video_name)
+c = 1
+if vc.isOpened():
+    rval,frame=vc.read()
+else:
+    rval=False
+while rval:
+    rval,frame=vc.read()
+    cv2.imwrite('./mask_face/IMG_'+str(c)+'.jpg',frame)
+    c=c+1
+    cv2.waitKey(1)
+vc.release()

+import colorsys
+
+import numpy as np
+from keras import backend as K
+from keras.models import load_model
+
+from yolo4.model import yolo_eval, Mish
+from yolo4.utils import letterbox_image
+import os
+from keras.utils import multi_gpu_model
+
+class YOLO(object):
+    def __init__(self):
+        self.model_path = os.getcwd() + '/../deep_sort_yolov4/model_data/yolo4_weight.h5'
+        self.anchors_path = os.getcwd() + '/../deep_sort_yolov4/model_data/yolo_anchors.txt'
+        self.classes_path = os.getcwd() + '/../deep_sort_yolov4/model_data/coco_classes.txt'
+        self.gpu_num = 1
+        self.score = 0.5
+        self.iou = 0.5
+        self.class_names = self._get_class()
+        self.anchors = self._get_anchors()
+        self.sess = K.get_session()
+        self.model_image_size = (608, 608)  # fixed size or (None, None)
+        self.is_fixed_size = self.model_image_size != (None, None)
+        self.boxes, self.scores, self.classes = self.generate()
+
+    def _get_class(self):
+        classes_path = os.path.expanduser(self.classes_path)
+        with open(classes_path) as f:
+            class_names = f.readlines()
+        class_names = [c.strip() for c in class_names]
+        return class_names
+
+    def _get_anchors(self):
+        anchors_path = os.path.expanduser(self.anchors_path)
+        with open(anchors_path) as f:
+            anchors = f.readline()
+            anchors = [float(x) for x in anchors.split(',')]
+            anchors = np.array(anchors).reshape(-1, 2)
+        return anchors
+
+    def generate(self):
+        model_path = os.path.expanduser(self.model_path)
+        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
+
+        self.yolo_model = load_model(model_path, custom_objects={'Mish': Mish}, compile=False)
+
+        print('{} model, anchors, and classes loaded.'.format(model_path))
+
+        # Generate colors for drawing bounding boxes.
+        hsv_tuples = [(x / len(self.class_names), 1., 1.)
+                      for x in range(len(self.class_names))]
+        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+        self.colors = list(
+            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
+                self.colors))
+        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
+        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
+        np.random.seed(None)  # Reset seed to default.
+
+        # Generate output tensor targets for filtered bounding boxes.
+        self.input_image_shape = K.placeholder(shape=(2, ))
+        if self.gpu_num>=2:
+            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
+        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
+                len(self.class_names), self.input_image_shape,
+                score_threshold=self.score, iou_threshold=self.iou)
+        return boxes, scores, classes
+
+    def detect_image(self, image):
+
+        if self.is_fixed_size:
+            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
+            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
+            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
+        else:
+            new_image_size = (image.width - (image.width % 32),
+                              image.height - (image.height % 32))
+            boxed_image = letterbox_image(image, new_image_size)
+        image_data = np.array(boxed_image, dtype='float32')
+
+        # print(image_data.shape)
+        image_data /= 255.
+        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.
+
+        out_boxes, out_scores, out_classes = self.sess.run(
+            [self.boxes, self.scores, self.classes],
+            feed_dict={
+                self.yolo_model.input: image_data,
+                self.input_image_shape: [image.size[1], image.size[0]],
+                K.learning_phase(): 0
+            })
+        return_boxes = []
+        return_scores = []
+        return_class_names = []
+        for i, c in reversed(list(enumerate(out_classes))):
+            predicted_class = self.class_names[c]
+            if predicted_class != 'person':  # Modify to detect other classes.
+                continue
+            box = out_boxes[i]
+            score = out_scores[i]
+            x = int(box[1])
+            y = int(box[0])
+            w = int(box[3] - box[1])
+            h = int(box[2] - box[0])
+            if x < 0:
+                w = w + x
+                x = 0
+            if y < 0:
+                h = h + y
+                y = 0
+            return_boxes.append([x, y, w, h])
+            return_scores.append(score)
+            return_class_names.append(predicted_class)
+
+        return return_boxes, return_scores, return_class_names
+
+    def close_session(self):
+        self.sess.close()

+"""Miscellaneous utility functions."""
+
+from functools import reduce
+
+from PIL import Image
+import numpy as np
+from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
+
+def compose(*funcs):
+    """Compose arbitrarily many functions, evaluated left to right.
+
+    Reference: https://mathieularose.com/function-composition-in-python/
+    """
+    # return lambda x: reduce(lambda v, f: f(v), funcs, x)
+    if funcs:
+        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
+    else:
+        raise ValueError('Composition of empty sequence not supported.')
+
+def letterbox_image(image, size):
+    '''resize image with unchanged aspect ratio using padding'''
+    iw, ih = image.size
+    w, h = size
+    scale = min(w/iw, h/ih)
+    nw = int(iw*scale)
+    nh = int(ih*scale)
+
+    image = image.resize((nw,nh), Image.BICUBIC)
+    new_image = Image.new('RGB', size, (128,128,128))
+    new_image.paste(image, ((w-nw)//2, (h-nh)//2))
+    return new_image
+
+def rand(a=0, b=1):
+    return np.random.rand()*(b-a) + a
+
+def get_random_data(annotation_line, input_shape, random=True, max_boxes=100, jitter=.3, hue=.1, sat=1.5, val=1.5, proc_img=True):
+    '''random preprocessing for real-time data augmentation'''
+    line = annotation_line.split()
+    image = Image.open(line[0])
+    iw, ih = image.size
+    h, w = input_shape
+    box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
+
+    if not random:
+        # resize image
+        scale = min(w/iw, h/ih)
+        nw = int(iw*scale)
+        nh = int(ih*scale)
+        dx = (w-nw)//2
+        dy = (h-nh)//2
+        image_data=0
+        if proc_img:
+            image = image.resize((nw,nh), Image.BICUBIC)
+            new_image = Image.new('RGB', (w,h), (128,128,128))
+            new_image.paste(image, (dx, dy))
+            image_data = np.array(new_image)/255.
+
+        # correct boxes
+        box_data = np.zeros((max_boxes,5))
+        if len(box)>0:
+            np.random.shuffle(box)
+            if len(box)>max_boxes: box = box[:max_boxes]
+            box[:, [0,2]] = box[:, [0,2]]*scale + dx
+            box[:, [1,3]] = box[:, [1,3]]*scale + dy
+            box_data[:len(box)] = box
+
+        return image_data, box_data
+
+    # resize image
+    new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter)
+    scale = rand(.25, 2)
+    if new_ar < 1:
+        nh = int(scale*h)
+        nw = int(nh*new_ar)
+    else:
+        nw = int(scale*w)
+        nh = int(nw/new_ar)
+    image = image.resize((nw,nh), Image.BICUBIC)
+
+    # place image
+    dx = int(rand(0, w-nw))
+    dy = int(rand(0, h-nh))
+    new_image = Image.new('RGB', (w,h), (128,128,128))
+    new_image.paste(image, (dx, dy))
+    image = new_image
+
+    # flip image or not
+    flip = rand()<.5
+    if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
+
+    # distort image
+    hue = rand(-hue, hue)
+    sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
+    val = rand(1, val) if rand()<.5 else 1/rand(1, val)
+    x = rgb_to_hsv(np.array(image)/255.)
+    x[..., 0] += hue
+    x[..., 0][x[..., 0]>1] -= 1
+    x[..., 0][x[..., 0]<0] += 1
+    x[..., 1] *= sat
+    x[..., 2] *= val
+    x[x>1] = 1
+    x[x<0] = 0
+    image_data = hsv_to_rgb(x) # numpy array, 0 to 1
+
+    # correct boxes
+    box_data = np.zeros((max_boxes,5))
+    if len(box)>0:
+        np.random.shuffle(box)
+        box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
+        box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
+        if flip: box[:, [0,2]] = w - box[:, [2,0]]
+        box[:, 0:2][box[:, 0:2]<0] = 0
+        box[:, 2][box[:, 2]>w] = w
+        box[:, 3][box[:, 3]>h] = h
+        box_w = box[:, 2] - box[:, 0]
+        box_h = box[:, 3] - box[:, 1]
+        box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
+        if len(box)>max_boxes: box = box[:max_boxes]
+        box_data[:len(box)] = box
+
+    return image_data, box_data

+absl-py==0.9.0
+asgiref==3.2.7
+astor==0.8.1
+astroid==2.3.3
+cachetools==4.1.0
+certifi==2020.4.5.1
+chardet==3.0.4
+colorama==0.4.3
+cycler==0.10.0
+Django==3.0.5
+et-xmlfile==1.0.1
+gast==0.2.2
+google-auth==1.14.1
+google-auth-oauthlib==0.4.1
+google-pasta==0.2.0
+grpcio==1.28.1
+h5py==2.10.0
+idna==2.9
+image==1.5.31
+isort==4.3.21
+jdcal==1.4.1
+joblib==0.14.1
+Keras==2.3.1
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+kiwisolver==1.2.0
+lazy-object-proxy==1.4.3
+Markdown==3.2.1
+matplotlib==3.2.1
+mccabe==0.6.1
+numpy==1.18.3
+oauthlib==3.1.0
+opencv-python==4.2.0.34
+openpyxl==3.0.3
+opt-einsum==3.2.1
+pandas==1.0.3
+Pillow==7.1.1
+protobuf==3.11.3
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pylint==2.4.4
+pyparsing==2.4.7
+python-dateutil==2.8.1
+pytz==2019.3
+PyYAML==5.3.1
+requests==2.23.0
+requests-oauthlib==1.3.0
+rsa==4.0
+scikit-learn==0.22.2.post1
+scipy==1.4.1
+seaborn==0.10.1
+six==1.14.0
+sklearn==0.0
+sqlparse==0.3.1
+tensorboard==1.15.0
+tensorflow==1.15.2
+tensorflow-estimator==1.15.1
+tensorflow-gpu==1.15.2
+tensorflow-gpu-estimator==2.1.0
+termcolor==1.1.0
+typed-ast==1.4.1
+urllib3==1.25.9
+Werkzeug==1.0.1
+wrapt==1.11.2
+xlrd==1.2.0

@@ -6,12 +6,14 @@
     "start": "node ./bin/www"
     "start": "node ./bin/www"
   },
   },
   "dependencies": {
   "dependencies": {
+    "child_process": "^1.0.2",
     "cookie-parser": "~1.4.4",
     "cookie-parser": "~1.4.4",
     "debug": "~2.6.9",
     "debug": "~2.6.9",
     "ejs": "~2.6.1",
     "ejs": "~2.6.1",
     "express": "~4.16.1",
     "express": "~4.16.1",
     "http-errors": "~1.6.3",
     "http-errors": "~1.6.3",
     "morgan": "~1.9.1",
     "morgan": "~1.9.1",
-    "multer": "^1.4.2"
+    "multer": "^1.4.2",
+    "python-shell": "^2.0.1"
   }
   }
 }
 }

@@ -2,10 +2,13 @@ var express = require('express');
 var router = express.Router();
 var router = express.Router();
 var multer = require("multer");
 var multer = require("multer");
 var path = require("path");
 var path = require("path");
+var PythonShell = require('python-shell');
+var spawn = require("child_process").spawn;
+
 
 
 var storage = multer.diskStorage({
 var storage = multer.diskStorage({
     destination: function(req, file, callback) {
     destination: function(req, file, callback) {
-        callback(null, "upload/")
+        callback(null, "../deep_sort_yolov4/")
     },
     },
     filename: function(req, file, callback) {
     filename: function(req, file, callback) {
         var extension = path.extname(file.originalname);
         var extension = path.extname(file.originalname);
@@ -21,20 +24,33 @@ var upload = multer({
 
 
 // 뷰 페이지 경로
 // 뷰 페이지 경로
 router.get('/', function(req, res, next) {
 router.get('/', function(req, res, next) {
-    res.render("index")
+    res.render('index', { title: 'Express' });
 });
 });
 
 
 // 2. 파일 업로드 처리
 // 2. 파일 업로드 처리
-router.post('/create', upload.single("File"), async(req, res) => {
+router.post('/create', upload.single("File"), function(req, res) {
     // 3. 파일 객체
     // 3. 파일 객체
-    var file = req.file
-
-    // 4. 파일 정보
-    var result = {
-        originalName: file.originalname,
-        size: file.size,
+    var file = req.file;
+    var options = {
+        mode: 'text',
+        pythonPath: __dirname + '/../../venv/Scripts/python.exe',
+        pythonOptions: ['-u'],
+        scriptPath: __dirname + '/../../deep_sort_yolov4/',
+        args: [file.originalname]
+    };
+    var shell1 = new PythonShell.PythonShell('main.py', options);
+    shell1.end(function(err) {
+        if (err) throw err;
+        else {
+            res.render('result', { file_name: file.originalname.split('.')[0] });
         }
         }
-
-    res.json(result);
+    });
+    // PythonShell.PythonShell.run('main.py', options, (err, results) => {
+    //     if (err) throw err;
+    //     PythonShell.PythonShell.run('kmean.py', options2, (err, results) => {
+    //         if (err) throw err;
+    //         res.render('result', { file_name: file.originalname.split('.')[0] });
+    //     });
+    // });
 });
 });
 module.exports = router;
 module.exports = router;
\ No newline at end of file

@@ -32,7 +32,8 @@
                 <div class="sidebar-brand-icon rotate-n-15">
                 <div class="sidebar-brand-icon rotate-n-15">
                     <i class="fas fa-thumbs-up"></i>
                     <i class="fas fa-thumbs-up"></i>
                 </div>
                 </div>
-                <div class="sidebar-brand-text mx-3">유동 인구 분석</div>
+                <div class="sidebar-brand-text mx-3">유동 인구 분석
+                </div>
             </a>
             </a>
 
 
             <!-- Divider -->
             <!-- Divider -->

@@ -86,10 +86,10 @@
                 <!-- /.container-fluid -->
                 <!-- /.container-fluid -->
                 <div class="container row">
                 <div class="container row">
                     <div class="container-video col-md-6">
                     <div class="container-video col-md-6">
-                        <video src="data/output2.avi" width="400" controls autoplay></video>
+                        <video src="data/<%=file_name%>.mp4" width="400" controls autoplay></video>
                     </div>
                     </div>
                     <div class="container-kmeans col-md-6">
                     <div class="container-kmeans col-md-6">
-                        <img src="data/test3_kmeans.png" style="width: 100%;" alt="Kmeans Image">
+                        <img src="data/<%=file_name%>_kmeans.png" style="width: 100%;" alt="Kmeans Image">
                     </div>
                     </div>
                 </div>
                 </div>
                 <div class="container-kibana"></div>
                 <div class="container-kibana"></div>
@@ -127,9 +127,6 @@
     <!-- Custom scripts for all pages-->
     <!-- Custom scripts for all pages-->
     <script src="javascripts/sb-admin-2.min.js"></script>
     <script src="javascripts/sb-admin-2.min.js"></script>
 
 
-    <!-- Page level plugins -->
-    <script src="vendor/chart.js/Chart.min.js"></script>
-
     <!-- Page level custom scripts -->
     <!-- Page level custom scripts -->
     <!-- <script src="javascripts/demo/chart-pie-demo.js"></script> -->
     <!-- <script src="javascripts/demo/chart-pie-demo.js"></script> -->
 </body>
 </body>