YOLOv3目标检测、卡尔曼滤波、匈牙利匹配算法多目标追踪

桃扇骨 2022-04-10 01:39 672阅读 0赞

转载自：[https://blog.csdn.net/xiao\_\_run/article/details/84374959][https_blog.csdn.net_xiao_run_article_details_84374959]

我先上下结果图吧，效果勉强还行，在这里我只训练了行人，官网的weights是coco数据集训练的，有80类；  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3hpYW9fX3J1bg_size_16_color_FFFFFF_t_70]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3hpYW9fX3J1bg_size_16_color_FFFFFF_t_70 1]  
**1、YOLOV3目标检测**  
关于yolov3的原理我在这里就不解释了，可谷歌学术自行阅读，说实话yolov3的效果着实不错，但是源码是C的，不依赖其他任何库，看的云里雾里，在这里我用的darknet训练的，利用tensorflow+keras进行测试的；  
关于tensorflow+keras版本yolov3，可参照

https://github.com/qqwweee/keras-yolo3

测试  
（1）获取训练好的权重

`wget https://pjreddie.com/media/files/yolov3.weights`

（2）转换 Darknet YOLO 模型为 Keras 模型

python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

转换过程如图  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3hpYW9fX3J1bg_size_16_color_FFFFFF_t_70 2]  
(3)运行目标检测测试代码  
`python yolo.py`

此时即可看到目标检测的效果

**2、卡尔曼滤波追踪**  
关于卡尔曼滤波的理论这里不打算讲了，就是那个5个基本的公式，这里直接给出公式：

公式1：X(k|k-1) = FX(k-1 | k-1) + BU(k) + W(k)

公式2：P(k|k-1) = FP(k-1|k-1)F’ + Q(k)

公式3：X(k|k) = X(k|k-1) + Kg(k)\[Z(k) - AX(k|k-1)

公式4：Kg(k) = P(k|k-1)A’/\{AP(k|k-1)A’ + R\} //卡尔曼增益

公式5：P(k|k) = (1- Kg(k) H) P(k|k-1)

下面给出Python版本的卡尔曼滤波小程序：

''' File name : kalman_filter.py File Description : Kalman Filter Algorithm Implementation Author : Srini Ananthakrishnan Date created : 07/14/2017 Date last modified: 07/16/2017 Python Version : 2.7 '''
    
    # Import python libraries
    import numpy as np
    
    class KalmanFilter(object):
        """Kalman Filter class keeps track of the estimated state of the system and the variance or uncertainty of the estimate. Predict and Correct methods implement the functionality Reference: https://en.wikipedia.org/wiki/Kalman_filter Attributes: None """
    
        def __init__(self):
            """Initialize variable used by Kalman Filter class Args: None Return: None """
            self.dt = 0.005  # delta time
    
            self.A = np.array([[1, 0], [0, 1]])  # matrix in observation equations
            self.u = np.zeros((2, 1))  # previous state vector
    
            # (x,y) tracking object center
            self.b = np.array([[0], [255]])  # vector of observations
    
            self.P = np.diag((3.0, 3.0))  # covariance matrix
            self.F = np.array([[1.0, self.dt], [0.0, 1.0]])  # state transition mat
    
            self.Q = np.eye(self.u.shape[0])  # process noise matrix
            self.R = np.eye(self.b.shape[0])  # observation noise matrix
            self.lastResult = np.array([[0], [255]])
    
        def predict(self):
            """Predict state vector u and variance of uncertainty P (covariance). where, u: previous state vector P: previous covariance matrix F: state transition matrix Q: process noise matrix Equations: u'_{k|k-1} = Fu'_{k-1|k-1} P_{k|k-1} = FP_{k-1|k-1} F.T + Q where, F.T is F transpose Args: None Return: vector of predicted state estimate """
            # Predicted state estimate
            self.u = np.round(np.dot(self.F, self.u))
            # Predicted estimate covariance
            self.P = np.dot(self.F, np.dot(self.P, self.F.T)) + self.Q
            self.lastResult = self.u  # same last predicted result
            return self.u
    
        def correct(self, b, flag):
            """Correct or update state vector u and variance of uncertainty P (covariance). where, u: predicted state vector u A: matrix in observation equations b: vector of observations P: predicted covariance matrix Q: process noise matrix R: observation noise matrix Equations: C = AP_{k|k-1} A.T + R K_{k} = P_{k|k-1} A.T(C.Inv) u'_{k|k} = u'_{k|k-1} + K_{k}(b_{k} - Au'_{k|k-1}) P_{k|k} = P_{k|k-1} - K_{k}(CK.T) where, A.T is A transpose C.Inv is C inverse Args: b: vector of observations flag: if "true" prediction result will be updated else detection Return: predicted state vector u """
    
            if not flag:  # update using prediction
                self.b = self.lastResult
            else:  # update using detection
                self.b = b
            C = np.dot(self.A, np.dot(self.P, self.A.T)) + self.R
            K = np.dot(self.P, np.dot(self.A.T, np.linalg.inv(C)))
    
            self.u = np.round(self.u + np.dot(K, (self.b - np.dot(self.A,
                                                                  self.u))))
            self.P = self.P - np.dot(K, np.dot(C, K.T))
            self.lastResult = self.u
            return self.u

**3、匈牙利匹配算法**  
匈牙利算法原理可参考这篇博客，写的还不错

https://blog.csdn.net/jingshushu1995/article/details/80411325

下面我给出我的卡尔曼滤波之后的匈牙利匹配算法的代码

''' File name : tracker.py File Description : Tracker Using Kalman Filter & Hungarian Algorithm Author : Srini Ananthakrishnan Date created : 07/14/2017 Date last modified: 07/16/2017 Python Version : 2.7 '''
    
    # Import python libraries
    import numpy as np
    from kalman_filter import KalmanFilter
    from common import dprint
    from scipy.optimize import linear_sum_assignment
    
    
    class Track(object):
        """Track class for every object to be tracked Attributes: None """
    
        def __init__(self, prediction, trackIdCount):
            """Initialize variables used by Track class Args: prediction: predicted centroids of object to be tracked trackIdCount: identification of each track object Return: None """
            self.track_id = trackIdCount  # identification of each track object
            self.KF = KalmanFilter()  # KF instance to track this object
            self.prediction = np.asarray(prediction)  # predicted centroids (x,y)
            self.skipped_frames = 0  # number of frames skipped undetected
            self.trace = []  # trace path
    
    
    class Tracker(object):
        """Tracker class that updates track vectors of object tracked Attributes: None """
    
        def __init__(self, dist_thresh, max_frames_to_skip, max_trace_length,
                     trackIdCount):
            """Initialize variable used by Tracker class Args: dist_thresh: distance threshold. When exceeds the threshold, track will be deleted and new track is created max_frames_to_skip: maximum allowed frames to be skipped for the track object undetected max_trace_lenght: trace path history length trackIdCount: identification of each track object Return: None """
            self.dist_thresh = dist_thresh
            self.max_frames_to_skip = max_frames_to_skip
            self.max_trace_length = max_trace_length
            self.tracks = []
            self.trackIdCount = trackIdCount
    
        def Update(self, detections):
            """Update tracks vector using following steps: - Create tracks if no tracks vector found - Calculate cost using sum of square distance between predicted vs detected centroids - Using Hungarian Algorithm assign the correct detected measurements to predicted tracks https://en.wikipedia.org/wiki/Hungarian_algorithm - Identify tracks with no assignment, if any - If tracks are not detected for long time, remove them - Now look for un_assigned detects - Start new tracks - Update KalmanFilter state, lastResults and tracks trace Args: detections: detected centroids of object to be tracked Return: None """
    
            # Create tracks if no tracks vector found
            if (len(self.tracks) == 0):
                for i in range(len(detections)):
                    track = Track(detections[i], self.trackIdCount)
                    self.trackIdCount += 1
                    self.tracks.append(track)
    
            # Calculate cost using sum of square distance between
            # predicted vs detected centroids
            N = len(self.tracks)
            M = len(detections)
            cost = np.zeros(shape=(N, M))   # Cost matrix
            for i in range(len(self.tracks)):
                for j in range(len(detections)):
                    try:
                        diff = self.tracks[i].prediction - detections[j]
                        distance = np.sqrt(diff[0][0]*diff[0][0] +
                                           diff[1][0]*diff[1][0])
                        cost[i][j] = distance
                    except:
                        pass
    
            # Let's average the squared ERROR
            cost = (0.5) * cost
            # Using Hungarian Algorithm assign the correct detected measurements
            # to predicted tracks
            assignment = []
            for _ in range(N):
                assignment.append(-1)
            row_ind, col_ind = linear_sum_assignment(cost)
            for i in range(len(row_ind)):
                assignment[row_ind[i]] = col_ind[i]
    
            # Identify tracks with no assignment, if any
            un_assigned_tracks = []
            for i in range(len(assignment)):
                if (assignment[i] != -1):
                    # check for cost distance threshold.
                    # If cost is very high then un_assign (delete) the track
                    if (cost[i][assignment[i]] > self.dist_thresh):
                        assignment[i] = -1
                        un_assigned_tracks.append(i)
                    pass
                else:
                    self.tracks[i].skipped_frames += 1
    
            # If tracks are not detected for long time, remove them
            del_tracks = []
            for i in range(len(self.tracks)):
                if (self.tracks[i].skipped_frames > self.max_frames_to_skip):
                    del_tracks.append(i)
            if len(del_tracks) > 0:  # only when skipped frame exceeds max
                for id in del_tracks:
                    if id < len(self.tracks):
                        del self.tracks[id]
                        del assignment[id]
                    else:
                        dprint("ERROR: id is greater than length of tracks")
    
            # Now look for un_assigned detects
            un_assigned_detects = []
            for i in range(len(detections)):
                    if i not in assignment:
                        un_assigned_detects.append(i)
    
            # Start new tracks
            if(len(un_assigned_detects) != 0):
                for i in range(len(un_assigned_detects)):
                    track = Track(detections[un_assigned_detects[i]],
                                  self.trackIdCount)
                    self.trackIdCount += 1
                    self.tracks.append(track)
    
            # Update KalmanFilter state, lastResults and tracks trace
            for i in range(len(assignment)):
                self.tracks[i].KF.predict()
    
                if(assignment[i] != -1):
                    self.tracks[i].skipped_frames = 0
                    self.tracks[i].prediction = self.tracks[i].KF.correct(
                                                detections[assignment[i]], 1)
                else:
                    self.tracks[i].prediction = self.tracks[i].KF.correct(
                                                np.array([[0], [0]]), 0)
    
                if(len(self.tracks[i].trace) > self.max_trace_length):
                    for j in range(len(self.tracks[i].trace) -
                                   self.max_trace_length):
                        del self.tracks[i].trace[j]
    
                self.tracks[i].trace.append(self.tracks[i].prediction)
                self.tracks[i].KF.lastResult = self.tracks[i].prediction

**4、整体代码**

# -*- coding: utf-8 -*-
    """ Class definition of YOLO_v3 style detection model on image and video """
    from tracker import Tracker
    import copy
    import colorsys
    import os
    from timeit import default_timer as timer
    import cv2
    import numpy as np
    from keras import backend as K
    from keras.models import load_model
    from keras.layers import Input
    from PIL import Image, ImageFont, ImageDraw
    
    from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
    from yolo3.utils import letterbox_image
    import os
    from keras.utils import multi_gpu_model
    
    class YOLO(object):
        _defaults = { 
            "model_path": 'model_data/yolov3.h5',
            "anchors_path": 'model_data/yolo_anchors.txt',
            "classes_path": 'model_data/coco_classes.txt',
            "score" : 0.3,
            "iou" : 0.45,
            "model_image_size" : (416, 416),
            "gpu_num" : 1,
        }
    
        @classmethod
        def get_defaults(cls, n):
            if n in cls._defaults:
                return cls._defaults[n]
            else:
                return "Unrecognized attribute name '" + n + "'"
    
        def __init__(self, **kwargs):
            self.__dict__.update(self._defaults) # set up default values
            self.__dict__.update(kwargs) # and update with user overrides
            self.class_names = self._get_class()
            self.anchors = self._get_anchors()
            self.sess = K.get_session()
            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(',')]
            return np.array(anchors).reshape(-1, 2)
    
        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.'
    
            # Load model, or construct model and load weights.
            num_anchors = len(self.anchors)
            num_classes = len(self.class_names)
            is_tiny_version = num_anchors==6 # default setting
            try:
                self.yolo_model = load_model(model_path, compile=False)
            except:
                self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                    if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
                self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
            else:
                assert self.yolo_model.layers[-1].output_shape[-1] == \
                    num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                    'Mismatch between model and given anchor and class sizes'
    
            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):
            start = timer()
            centers=[]
            if self.model_image_size != (None, None):
                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
                })
    
            #print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
    
            font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                        size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
            thickness = (image.size[0] + image.size[1]) // 300
    
            for i, c in reversed(list(enumerate(out_classes))):
                predicted_class = self.class_names[c]
                box = out_boxes[i]
                score = out_scores[i]
                number=len(out_boxes)
                label = '{} {:.2f}'.format(predicted_class, score)
                draw = ImageDraw.Draw(image)
                label_size = draw.textsize(label, font)
                if c==0:
                    top, left, bottom, right = box
                    center=(int ((left+right)//2),int((top+bottom)//2))
                    b=np.array([[(left+right)//2],[(top+bottom)//2]])
    
                    centers.append(b)
                    top = max(0, np.floor(top + 0.5).astype('int32'))
                    left = max(0, np.floor(left + 0.5).astype('int32'))
                    bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
                    right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
                    #print(label, (left, top), (right, bottom))
    
                    if top - label_size[1] >= 0:
                        text_origin = np.array([left, top - label_size[1]])
                    else:
                        text_origin = np.array([left, top + 1])
    
                # My kingdom for a good redistributable image drawing library.
                    for i in range(thickness):
                        draw.rectangle(
                            [left + i, top + i, right - i, bottom - i],
                            outline=self.colors[c])
                    draw.rectangle(
                        [tuple(text_origin), tuple(text_origin + label_size)],
                        fill=self.colors[c])
                    #draw.rectangle(
                        #[tuple(text_origin), tuple(text_origin + label_size)],
                        #fill=self.colors[c])
                    draw.text(text_origin, label, fill=(0, 0, 0), font=font)
    
                    del draw
    
            end = timer()
            #print(end - start)
            return image,centers,number
    
        def close_session(self):
            self.sess.close()
    
    def detect_video(yolo, video_path, output_path=""):
    
        vid = cv2.VideoCapture(video_path)
        if not vid.isOpened():
            raise IOError("Couldn't open webcam or video")
        video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))
        video_fps       = vid.get(cv2.CAP_PROP_FPS)
        video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
                            int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
        isOutput = True if output_path != "" else False
        if isOutput:
            #print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
            out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
        accum_time = 0
        curr_fps = 0
        fps = "FPS: ??"
        prev_time = timer()
        tracker = Tracker(160, 30, 6, 100)
        # Variables initialization
        skip_frame_count = 0
        track_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
                        (0, 255, 255), (255, 0, 255), (255, 127, 255),
                        (127, 0, 255), (127, 0, 127)]
        pause = False
        while True:
            return_value, frame = vid.read()
            print(frame.shape)
            image = Image.fromarray(frame)
            image,centers,number = yolo.detect_image(image)
            print(image.size)
            result = np.asarray(image)
            curr_time = timer()
            exec_time = curr_time - prev_time
            prev_time = curr_time
            accum_time = accum_time + exec_time
            curr_fps = curr_fps + 1
            if accum_time > 1:
                accum_time = accum_time - 1
                fps = "FPS: " + str(curr_fps)
                curr_fps = 0
            font = cv2.FONT_HERSHEY_SIMPLEX
            #cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=0.50, color=(255, 0, 0), thickness=2)
            cv2.putText(result, str(number), (20,  40), font, 1, (0, 0, 255), 5)
            #for k in range(len(centers)):
    
                #cv2.circle(frame, centers[k], 3, (255, 255, 0), 3)
                #cv2.imshow("xiao",frame)
            #cv2.waitKey(100000)
            ############################################################################################
            #print(len(centers))
            #for i in range(len(centers)):
                #print(centers[i])
                #cv2.waitKey(0)
            if (len(centers) > 0):
    
                # Track object using Kalman Filter
                tracker.Update(centers)
    
                # For identified object tracks draw tracking line
                # Use various colors to indicate different track_id
                for i in range(len(tracker.tracks)):
                    if (len(tracker.tracks[i].trace) > 1):
                        for j in range(len(tracker.tracks[i].trace) - 1):
                            # Draw trace line
                            x1 = tracker.tracks[i].trace[j][0][0]
                            y1 = tracker.tracks[i].trace[j][1][0]
                            x2 = tracker.tracks[i].trace[j + 1][0][0]
                            y2 = tracker.tracks[i].trace[j + 1][1][0]
    
                            clr = tracker.tracks[i].track_id % 9
                            cv2.line(result, (int(x1), int(y1)), (int(x2), int(y2)),
                                     track_colors[clr], 4)
                            #x3 = tracker.tracks[i].track_id
                            #cv2.putText(result,str(tracker.tracks[j].track_id),(int(x1),int(y1)),font,track_colors[j],3)
                            #cv2.circle(result,(int(x1),int(y1)),3,track_colors[j],3)
                # Display the resulting tracking frame
                cv2.imshow('Tracking', result)
                ###################################################
            cv2.namedWindow("result", cv2.WINDOW_NORMAL)
            cv2.imshow("result", result)
            if isOutput:
                out.write(result)
            if cv2.waitKey(100) & 0xFF == ord('q'):
                break
        yolo.close_session()

欢迎大家自己做修改，代码规范 并不好，勉强能够学习一下，后面我会再进行修改。

[https_blog.csdn.net_xiao_run_article_details_84374959]: https://blog.csdn.net/xiao__run/article/details/84374959
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