目标检测之车辆行人（tensorflow版yolov3）

背景：

在自动驾驶中，基于摄像头的视觉感知，如同人的眼睛一样重要。而目前主流方案基本都采用深度学习方案（tensorflow等），而非传统图像处理（opencv等）。

　　接下来我们就以YOLOV3为基本网络模型，Tensorflow为基本框架，搭建一套能够自动识别路面上动态目标，如车辆，行人，骑行人等。

正文：

　　原生YOLOV3是基于darknet（纯C编写）开发的，这里我们会将YOLOV3架构在Tensorflow平台上（Python,C++跨平台多语言）。

关键点介绍：

一、基本的网络结构图：

模型流程图如下：

　　基础主干网Darknet53：

二、代码结构：

tf_yolov3

|-------extract_voc.py #从原始文件中生成训练所需数据格式

import os
import argparse
import xml.etree.ElementTree as ET

# sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
sets=[('', 'train'), ('', 'val')]

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

parser = argparse.ArgumentParser()
parser.add_argument("--voc_path", default="/home/yang/test/VOCdevkit/train/")
parser.add_argument("--dataset_info_path", default="./")
flags = parser.parse_args()

def convert_annotation(year, image_id, list_file):
    xml_path = os.path.join(flags.voc_path, 'VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
    in_file = open(xml_path)
    tree=ET.parse(in_file)
    root = tree.getroot()

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult)==1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text))
        list_file.write(" " +  " ".join([str(a) for a in b]) + " " + str(cls_id))

for year, image_set in sets:
    text_path = os.path.join(flags.voc_path, 'VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set))
    if not os.path.exists(text_path): continue
    image_ids = open(text_path).read().strip().split()
    list_file_path = os.path.join(flags.dataset_info_path, '%s_%s.txt'%(year, image_set))
    list_file = open(list_file_path, 'w')
    for image_id in image_ids:
        image_path = os.path.join(flags.voc_path, 'VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(year, image_id))
        print("=>", image_path)
        list_file.write(image_path)
        convert_annotation(year, image_id, list_file)
        list_file.write('\n')
    list_file.close()

　　|-------make_voc_tfrecord.sh  #制作tfrecord格式

python scripts/extract_voc.py --voc_path /home/yang/test/VOC/train/ --dataset_info_path ./
cat ./2007_train.txt ./2007_val.txt > voc_train.txt
python scripts/extract_voc.py --voc_path /home/yang/test/VOC/test/ --dataset_info_path ./
cat ./2007_test.txt > voc_test.txt
python core/convert_tfrecord.py --dataset_txt ./voc_train.txt --tfrecord_path_prefix /home/yang/test/VOC/train/voc_train
python core/convert_tfrecord.py --dataset_txt ./voc_test.txt  --tfrecord_path_prefix /home/yang/test/VOC/test/voc_test

|-------convert_tfrecord.py # 转换原始数据成TFReocord格式

import sys
import argparse
import numpy as np
import tensorflow as tf

def main(argv):
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset_txt", default='./data/dataset.txt')
    parser.add_argument("--tfrecord_path_prefix", default='./data/tfrecords/dataset')
    flags = parser.parse_args()

    dataset = {}
    #
    with open(flags.dataset_txt,'r') as f:
        for line in f.readlines():
            example = line.split(' ')
            image_path = example[0]
            boxes_num = len(example[1:]) // 5
            boxes = np.zeros([boxes_num, 5], dtype=np.float32)
            for i in range(boxes_num):
                boxes[i] = example[1+i*5:6+i*5]
            dataset[image_path] = boxes

    image_paths = list(dataset.keys())
    images_num = len(image_paths)
    print(">> Processing %d images" %images_num)

    tfrecord_file = flags.tfrecord_path_prefix+".tfrecords"
    with tf.python_io.TFRecordWriter(tfrecord_file) as record_writer:
        for i in range(images_num):
            image = tf.gfile.FastGFile(image_paths[i], 'rb').read()
            boxes = dataset[image_paths[i]]
            boxes = boxes.tostring()

            example = tf.train.Example(features = tf.train.Features(
                feature={
                    'image' :tf.train.Feature(bytes_list = tf.train.BytesList(value = [image])),
                    'boxes' :tf.train.Feature(bytes_list = tf.train.BytesList(value = [boxes])),
                }
            ))

            record_writer.write(example.SerializeToString())
        print(">> Saving %d images in %s" %(images_num, tfrecord_file))

|-------dataset.py # 图片数据增强，数据加载

import cv2
import numpy as np
from core import utils
import tensorflow as tf

class Parser(object):
    def __init__(self, image_h, image_w, anchors, num_classes, debug=False):

        self.anchors     = anchors
        self.num_classes = num_classes
        self.image_h     = image_h
        self.image_w     = image_w
        self.debug       = debug

    def flip_left_right(self, image, gt_boxes):

        w = tf.cast(tf.shape(image)[1], tf.float32)
        image = tf.image.flip_left_right(image)

        xmin, ymin, xmax, ymax, label = tf.unstack(gt_boxes, axis=1)
        xmin, ymin, xmax, ymax = w-xmax, ymin, w-xmin, ymax
        gt_boxes = tf.stack([xmin, ymin, xmax, ymax, label], axis=1)

        return image, gt_boxes

    def random_distort_color(self, image, gt_boxes):

        image = tf.image.random_brightness(image, max_delta=32./255.)
        image = tf.image.random_saturation(image, lower=0.8, upper=1.2)
        image = tf.image.random_hue(image, max_delta=0.2)
        image = tf.image.random_contrast(image, lower=0.8, upper=1.2)

        return image, gt_boxes

    def random_blur(self, image, gt_boxes):

        gaussian_blur = lambda image: cv2.GaussianBlur(image, (5, 5), 0)
        h, w = image.shape.as_list()[:2]
        image = tf.py_func(gaussian_blur, [image], tf.uint8)
        image.set_shape([h, w, 3])

        return image, gt_boxes

    def random_crop(self, image, gt_boxes, min_object_covered=0.8, aspect_ratio_range=[0.8, 1.2], area_range=[0.5, 1.0]):

        h, w = tf.cast(tf.shape(image)[0], tf.float32), tf.cast(tf.shape(image)[1], tf.float32)
        xmin, ymin, xmax, ymax, label = tf.unstack(gt_boxes, axis=1)
        bboxes = tf.stack([ ymin/h, xmin/w, ymax/h, xmax/w], axis=1)
        bboxes = tf.clip_by_value(bboxes, 0, 1)
        begin, size, dist_boxes = tf.image.sample_distorted_bounding_box(
                                        tf.shape(image),
                                        bounding_boxes=tf.expand_dims(bboxes, axis=0),
                                        min_object_covered=min_object_covered,
                                        aspect_ratio_range=aspect_ratio_range,
                                        area_range=area_range)
        # NOTE dist_boxes with shape: [ymin, xmin, ymax, xmax] and in values in range(0, 1)
        # Employ the bounding box to distort the image.
        croped_box = [dist_boxes[0,0,1]*w, dist_boxes[0,0,0]*h, dist_boxes[0,0,3]*w, dist_boxes[0,0,2]*h]

        croped_xmin = tf.clip_by_value(xmin, croped_box[0], croped_box[2])-croped_box[0]
        croped_ymin = tf.clip_by_value(ymin, croped_box[1], croped_box[3])-croped_box[1]
        croped_xmax = tf.clip_by_value(xmax, croped_box[0], croped_box[2])-croped_box[0]
        croped_ymax = tf.clip_by_value(ymax, croped_box[1], croped_box[3])-croped_box[1]

        image = tf.slice(image, begin, size)
        gt_boxes = tf.stack([croped_xmin, croped_ymin, croped_xmax, croped_ymax, label], axis=1)

        return image, gt_boxes

    def preprocess(self, image, gt_boxes):

        ################################# data augmentation ##################################
        # data_aug_flag = tf.to_int32(tf.random_uniform(shape=[], minval=-5, maxval=5))

        # caseO = tf.equal(data_aug_flag, 1), lambda: self.flip_left_right(image, gt_boxes)
        # case1 = tf.equal(data_aug_flag, 2), lambda: self.random_distort_color(image, gt_boxes)
        # case2 = tf.equal(data_aug_flag, 3), lambda: self.random_blur(image, gt_boxes)
        # case3 = tf.equal(data_aug_flag, 4), lambda: self.random_crop(image, gt_boxes)

        # image, gt_boxes = tf.case([caseO, case1, case2, case3], lambda: (image, gt_boxes))

        image, gt_boxes = utils.resize_image_correct_bbox(image, gt_boxes, self.image_h, self.image_w)

        if self.debug: return image, gt_boxes

        y_true_13, y_true_26, y_true_52 = tf.py_func(self.preprocess_true_boxes, inp=[gt_boxes],
                            Tout = [tf.float32, tf.float32, tf.float32])
        image = image / 255.

        return image, y_true_13, y_true_26, y_true_52

    def preprocess_true_boxes(self, gt_boxes):
        """
        Preprocess true boxes to training input format
        Parameters:
        -----------
        :param true_boxes: numpy.ndarray of shape [T, 4]
                            T: the number of boxes in each image.
                            4: coordinate => x_min, y_min, x_max, y_max
        :param true_labels: class id
        :param input_shape: the shape of input image to the yolov3 network, [416, 416]
        :param anchors: array, shape=[9,2], 9: the number of anchors, 2: width, height
        :param num_classes: integer, for coco dataset, it is 80
        Returns:
        ----------
        y_true: list(3 array), shape like yolo_outputs, [13, 13, 3, 85]
                            13:cell szie, 3:number of anchors
                            85: box_centers, box_sizes, confidence, probability
        """
        num_layers = len(self.anchors) // 3
        anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
        grid_sizes = [[self.image_h//x, self.image_w//x] for x in (32, 16, 8)]

        box_centers = (gt_boxes[:, 0:2] + gt_boxes[:, 2:4]) / 2 # the center of box
        box_sizes =    gt_boxes[:, 2:4] - gt_boxes[:, 0:2] # the height and width of box

        gt_boxes[:, 0:2] = box_centers
        gt_boxes[:, 2:4] = box_sizes

        y_true_13 = np.zeros(shape=[grid_sizes[0][0], grid_sizes[0][1], 3, 5+self.num_classes], dtype=np.float32)
        y_true_26 = np.zeros(shape=[grid_sizes[1][0], grid_sizes[1][1], 3, 5+self.num_classes], dtype=np.float32)
        y_true_52 = np.zeros(shape=[grid_sizes[2][0], grid_sizes[2][1], 3, 5+self.num_classes], dtype=np.float32)

        y_true = [y_true_13, y_true_26, y_true_52]
        anchors_max =  self.anchors / 2.
        anchors_min = -anchors_max
        valid_mask = box_sizes[:, 0] > 0

        # Discard zero rows.
        wh = box_sizes[valid_mask]
        # set the center of all boxes as the origin of their coordinates
        # and correct their coordinates
        wh = np.expand_dims(wh, -2)
        boxes_max = wh / 2.
        boxes_min = -boxes_max

        intersect_mins = np.maximum(boxes_min, anchors_min)
        intersect_maxs = np.minimum(boxes_max, anchors_max)
        intersect_wh   = np.maximum(intersect_maxs - intersect_mins, 0.)
        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
        box_area       = wh[..., 0] * wh[..., 1]

        anchor_area = self.anchors[:, 0] * self.anchors[:, 1]
        iou = intersect_area / (box_area + anchor_area - intersect_area)
        # Find best anchor for each true box
        best_anchor = np.argmax(iou, axis=-1)

        for t, n in enumerate(best_anchor):
            for l in range(num_layers):
                if n not in anchor_mask[l]: continue

                i = np.floor(gt_boxes[t,0]/self.image_w*grid_sizes[l][1]).astype('int32')
                j = np.floor(gt_boxes[t,1]/self.image_h*grid_sizes[l][0]).astype('int32')

                k = anchor_mask[l].index(n)
                c = gt_boxes[t, 4].astype('int32')

                y_true[l][j, i, k, 0:4] = gt_boxes[t, 0:4]
                y_true[l][j, i, k,   4] = 1.
                y_true[l][j, i, k, 5+c] = 1.

        return y_true_13, y_true_26, y_true_52

    def parser_example(self, serialized_example):

        features = tf.parse_single_example(
            serialized_example,
            features = {
                'image' : tf.FixedLenFeature([], dtype = tf.string),
                'boxes' : tf.FixedLenFeature([], dtype = tf.string),
            }
        )

        image = tf.image.decode_jpeg(features['image'], channels = 3)
        image = tf.image.convert_image_dtype(image, tf.uint8)

        gt_boxes = tf.decode_raw(features['boxes'], tf.float32)
        gt_boxes = tf.reshape(gt_boxes, shape=[-1,5])

        return self.preprocess(image, gt_boxes)

class dataset(object):
    def __init__(self, parser, tfrecords_path, batch_size, shuffle=None, repeat=True):
        self.parser = parser
        self.filenames = tf.gfile.Glob(tfrecords_path)
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.repeat  = repeat
        self._buildup()

    def _buildup(self):
        try:
            self._TFRecordDataset = tf.data.TFRecordDataset(self.filenames)
        except:
            raise NotImplementedError("No tfrecords found!")

        self._TFRecordDataset = self._TFRecordDataset.map(map_func = self.parser.parser_example,
                                                        num_parallel_calls = 10)
        self._TFRecordDataset = self._TFRecordDataset.repeat() if self.repeat else self._TFRecordDataset

        if self.shuffle is not None:
            self._TFRecordDataset = self._TFRecordDataset.shuffle(self.shuffle)

        self._TFRecordDataset = self._TFRecordDataset.batch(self.batch_size).prefetch(self.batch_size)
        self._iterator = self._TFRecordDataset.make_one_shot_iterator()

    def get_next(self):
        return self._iterator.get_next()

　　|-------utils.py # NMS,画检测框，从PB文件读取Tensors，计算MAP，FREEZE_GRAPH,加载权重文件，IOU计算

import colorsys
import numpy as np
import tensorflow as tf
from collections import Counter
from PIL import ImageFont, ImageDraw

# Discard all boxes with low scores and high IOU
def gpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.3, iou_thresh=0.5):
    """
    /*----------------------------------- NMS on gpu ---------------------------------------*/

    Arguments:
            boxes  -- tensor of shape [1, 10647, 4] # 10647 boxes
            scores -- tensor of shape [1, 10647, num_classes], scores of boxes
            classes -- the return value of function `read_coco_names`
    Note:Applies Non-max suppression (NMS) to set of boxes. Prunes away boxes that have high
    intersection-over-union (IOU) overlap with previously selected boxes.

    max_boxes -- integer, maximum number of predicted boxes you'd like, default is 20
    score_thresh -- real value, if [ highest class probability score < score_threshold]
                       then get rid of the corresponding box
    iou_thresh -- real value, "intersection over union" threshold used for NMS filtering
    """

    boxes_list, label_list, score_list = [], [], []
    max_boxes = tf.constant(max_boxes, dtype='int32')

    # since we do nms for single image, then reshape it
    boxes = tf.reshape(boxes, [-1,4]) # '-1' means we don't konw the exact number of boxes
    # confs = tf.reshape(confs, [-1,1])
    score = tf.reshape(scores, [-1,num_classes])

    # Step 1: Create a filtering mask based on "box_class_scores" by using "threshold".
    mask = tf.greater_equal(score, tf.constant(score_thresh))
    # Step 2: Do non_max_suppression for each class
    for i in range(num_classes):
        # Step 3: Apply the mask to scores, boxes and pick them out
        filter_boxes = tf.boolean_mask(boxes, mask[:,i])
        filter_score = tf.boolean_mask(score[:,i], mask[:,i])
        nms_indices = tf.image.non_max_suppression(boxes=filter_boxes,
                                                   scores=filter_score,
                                                   max_output_size=max_boxes,
                                                   iou_threshold=iou_thresh, name='nms_indices')
        label_list.append(tf.ones_like(tf.gather(filter_score, nms_indices), 'int32')*i)
        boxes_list.append(tf.gather(filter_boxes, nms_indices))
        score_list.append(tf.gather(filter_score, nms_indices))

    boxes = tf.concat(boxes_list, axis=0)
    score = tf.concat(score_list, axis=0)
    label = tf.concat(label_list, axis=0)

    return boxes, score, label

def py_nms(boxes, scores, max_boxes=50, iou_thresh=0.5):
    """
    Pure Python NMS baseline.

    Arguments: boxes => shape of [-1, 4], the value of '-1' means that dont know the
                        exact number of boxes
               scores => shape of [-1,]
               max_boxes => representing the maximum of boxes to be selected by non_max_suppression
               iou_thresh => representing iou_threshold for deciding to keep boxes
    """
    assert boxes.shape[1] == 4 and len(scores.shape) == 1

    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        inds = np.where(ovr <= iou_thresh)[0]
        order = order[inds + 1]

    return keep[:max_boxes]

def cpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.3, iou_thresh=0.5):
    """
    /*----------------------------------- NMS on cpu ---------------------------------------*/
    Arguments:
        boxes ==> shape [1, 10647, 4]
        scores ==> shape [1, 10647, num_classes]
    """

    boxes = boxes.reshape(-1, 4)
    scores = scores.reshape(-1, num_classes)
    # Picked bounding boxes
    picked_boxes, picked_score, picked_label = [], [], []

    for i in range(num_classes):
        indices = np.where(scores[:,i] >= score_thresh)
        filter_boxes = boxes[indices]
        filter_scores = scores[:,i][indices]
        if len(filter_boxes) == 0: continue
        # do non_max_suppression on the cpu
        indices = py_nms(filter_boxes, filter_scores,
                         max_boxes=max_boxes, iou_thresh=iou_thresh)
        picked_boxes.append(filter_boxes[indices])
        picked_score.append(filter_scores[indices])
        picked_label.append(np.ones(len(indices), dtype='int32')*i)
    if len(picked_boxes) == 0: return None, None, None

    boxes = np.concatenate(picked_boxes, axis=0)
    score = np.concatenate(picked_score, axis=0)
    label = np.concatenate(picked_label, axis=0)

    return boxes, score, label

def resize_image_correct_bbox(image, boxes, image_h, image_w):

    origin_image_size = tf.to_float(tf.shape(image)[0:2])
    image = tf.image.resize_images(image, size=[image_h, image_w])

    # correct bbox
    xx1 = boxes[:, 0] * image_w / origin_image_size[1]
    yy1 = boxes[:, 1] * image_h / origin_image_size[0]
    xx2 = boxes[:, 2] * image_w / origin_image_size[1]
    yy2 = boxes[:, 3] * image_h / origin_image_size[0]
    idx = boxes[:, 4]

    boxes = tf.stack([xx1, yy1, xx2, yy2, idx], axis=1)
    return image, boxes

def draw_boxes(image, boxes, scores, labels, classes, detection_size,
               font='./data/font/FiraMono-Medium.otf', show=True):
    """
    :param boxes, shape of  [num, 4]
    :param scores, shape of [num, ]
    :param labels, shape of [num, ]
    :param image,
    :param classes, the return list from the function `read_coco_names`
    """
    if boxes is None: return image
    draw = ImageDraw.Draw(image)
    # draw settings
    font = ImageFont.truetype(font = font, size = np.floor(2e-2 * image.size[1]).astype('int32'))
    hsv_tuples = [( x / len(classes), 0.9, 1.0) for x in range(len(classes))]
    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
    for i in range(len(labels)): # for each bounding box, do:
        bbox, score, label = boxes[i], scores[i], classes[labels[i]]
        bbox_text = "%s %.2f" %(label, score)
        text_size = draw.textsize(bbox_text, font)
        # convert_to_original_size
        detection_size, original_size = np.array(detection_size), np.array(image.size)
        ratio = original_size / detection_size
        bbox = list((bbox.reshape(2,2) * ratio).reshape(-1))

        draw.rectangle(bbox, outline=colors[labels[i]], width=3)
        text_origin = bbox[:2]-np.array([0, text_size[1]])
        draw.rectangle([tuple(text_origin), tuple(text_origin+text_size)], fill=colors[labels[i]])
        # # draw bbox
        draw.text(tuple(text_origin), bbox_text, fill=(0,0,0), font=font)

    image.show() if show else None
    return image

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

def freeze_graph(sess, output_file, output_node_names):

    output_graph_def = tf.graph_util.convert_variables_to_constants(
        sess,
        sess.graph.as_graph_def(),
        output_node_names,
    )

    with tf.gfile.GFile(output_file, "wb") as f:
        f.write(output_graph_def.SerializeToString())

    print("=> {} ops written to {}.".format(len(output_graph_def.node), output_file))

def read_pb_return_tensors(graph, pb_file, return_elements):

    with tf.gfile.FastGFile(pb_file, 'rb') as f:
        frozen_graph_def = tf.GraphDef()
        frozen_graph_def.ParseFromString(f.read())

    with graph.as_default():
        return_elements = tf.import_graph_def(frozen_graph_def,
                                              return_elements=return_elements)
        input_tensor, output_tensors = return_elements[0], return_elements[1:]

    return input_tensor, output_tensors

def load_weights(var_list, weights_file):
    """
    Loads and converts pre-trained weights.
    :param var_list: list of network variables.
    :param weights_file: name of the binary file.
    :return: list of assign ops
    """
    with open(weights_file, "rb") as fp:
        np.fromfile(fp, dtype=np.int32, count=5)
        weights = np.fromfile(fp, dtype=np.float32)

    ptr = 0
    i = 0
    assign_ops = []
    while i < len(var_list) - 1:
        var1 = var_list[i]
        print("=> loading ", var1.name)
        var2 = var_list[i + 1]
        print("=> loading ", var2.name)
        # do something only if we process conv layer
        if 'Conv' in var1.name.split('/')[-2]:
            # check type of next layer
            if 'BatchNorm' in var2.name.split('/')[-2]:
                # load batch norm params
                gamma, beta, mean, var = var_list[i + 1:i + 5]
                batch_norm_vars = [beta, gamma, mean, var]
                for var in batch_norm_vars:
                    shape = var.shape.as_list()
                    num_params = np.prod(shape)
                    var_weights = weights[ptr:ptr + num_params].reshape(shape)
                    ptr += num_params
                    assign_ops.append(tf.assign(var, var_weights, validate_shape=True))
                # we move the pointer by 4, because we loaded 4 variables
                i += 4
            elif 'Conv' in var2.name.split('/')[-2]:
                # load biases
                bias = var2
                bias_shape = bias.shape.as_list()
                bias_params = np.prod(bias_shape)
                bias_weights = weights[ptr:ptr +
                                       bias_params].reshape(bias_shape)
                ptr += bias_params
                assign_ops.append(tf.assign(bias, bias_weights, validate_shape=True))
                # we loaded 1 variable
                i += 1
            # we can load weights of conv layer
            shape = var1.shape.as_list()
            num_params = np.prod(shape)

            var_weights = weights[ptr:ptr + num_params].reshape(
                (shape[3], shape[2], shape[0], shape[1]))
            # remember to transpose to column-major
            var_weights = np.transpose(var_weights, (2, 3, 1, 0))
            ptr += num_params
            assign_ops.append(
                tf.assign(var1, var_weights, validate_shape=True))
            i += 1

    return assign_ops

def get_anchors(anchors_path, image_h, image_w):
    '''loads the anchors from a file'''
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = np.array(anchors.split(), dtype=np.float32)
    anchors = anchors.reshape(-1,2)
    anchors[:, 1] = anchors[:, 1] * image_h
    anchors[:, 0] = anchors[:, 0] * image_w
    return anchors.astype(np.int32)

def bbox_iou(A, B):

    intersect_mins = np.maximum(A[:, 0:2], B[:, 0:2])
    intersect_maxs = np.minimum(A[:, 2:4], B[:, 2:4])
    intersect_wh   = np.maximum(intersect_maxs - intersect_mins, 0.)
    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]

    A_area = np.prod(A[:, 2:4] - A[:, 0:2], axis=1)
    B_area = np.prod(B[:, 2:4] - B[:, 0:2], axis=1)

    iou = intersect_area / (A_area + B_area - intersect_area)

    return iou

def evaluate(y_pred, y_true, iou_thresh=0.5, score_thresh=0.3):

    num_images  = y_true[0].shape[0]
    num_classes = y_true[0][0][..., 5:].shape[-1]
    true_labels_dict   = {i:0 for i in range(num_classes)} # {class: count}
    pred_labels_dict   = {i:0 for i in range(num_classes)}
    true_positive_dict = {i:0 for i in range(num_classes)}

    for i in range(num_images):
        true_labels_list, true_boxes_list = [], []
        for j in range(3): # three feature maps
            true_probs_temp = y_true[j][i][...,5: ]
            true_boxes_temp = y_true[j][i][...,0:4]

            object_mask = true_probs_temp.sum(axis=-1) > 0

            true_probs_temp = true_probs_temp[object_mask]
            true_boxes_temp = true_boxes_temp[object_mask]

            true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()
            true_boxes_list  += true_boxes_temp.tolist()

        if len(true_labels_list) != 0:
            for cls, count in Counter(true_labels_list).items(): true_labels_dict[cls] += count

        pred_boxes = y_pred[0][i:i+1]
        pred_confs = y_pred[1][i:i+1]
        pred_probs = y_pred[2][i:i+1]

        pred_boxes, pred_scores, pred_labels = cpu_nms(pred_boxes, pred_confs*pred_probs, num_classes,
                                                      score_thresh=score_thresh, iou_thresh=iou_thresh)

        true_boxes = np.array(true_boxes_list)
        box_centers, box_sizes = true_boxes[:,0:2], true_boxes[:,2:4]

        true_boxes[:,0:2] = box_centers - box_sizes / 2.
        true_boxes[:,2:4] = true_boxes[:,0:2] + box_sizes
        pred_labels_list = [] if pred_labels is None else pred_labels.tolist()

        if len(pred_labels_list) != 0:
            for cls, count in Counter(pred_labels_list).items(): pred_labels_dict[cls] += count
        else:
            continue

        detected = []
        for k in range(len(pred_labels_list)):
            # compute iou between predicted box and ground_truth boxes
            iou = bbox_iou(pred_boxes[k:k+1], true_boxes)
            m = np.argmax(iou) # Extract index of largest overlap
            if iou[m] >= iou_thresh and pred_labels_list[k] == true_labels_list[m] and m not in detected:
                true_positive_dict[true_labels_list[m]] += 1
                detected.append(m)

    recall    = sum(true_positive_dict.values()) / (sum(true_labels_dict.values()) + 1e-6)
    precision = sum(true_positive_dict.values()) / (sum(pred_labels_dict.values()) + 1e-6)

    return recall, precision

def compute_ap(recall, precision):
    """ Compute the average precision, given the recall and precision curves.
    Code originally from https://github.com/rbgirshick/py-faster-rcnn.
    # Arguments
        recall:    The recall curve (list).
        precision: The precision curve (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """
    # correct AP calculation
    # first append sentinel values at the end
    mrec = np.concatenate(([0.0], recall, [1.0]))
    mpre = np.concatenate(([0.0], precision, [0.0]))

    # compute the precision envelope
    for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

    # to calculate area under PR curve, look for points
    # where X axis (recall) changes value
    i = np.where(mrec[1:] != mrec[:-1])[0]

    # and sum (\Delta recall) * prec
    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap

　　|-------common.py # 卷积层Pad 使用Slim

import tensorflow as tf
slim = tf.contrib.slim

def _conv2d_fixed_padding(inputs, filters, kernel_size, strides=1):
    if strides > 1: inputs = _fixed_padding(inputs, kernel_size)
    inputs = slim.conv2d(inputs, filters, kernel_size, stride=strides,
                         padding=('SAME' if strides == 1 else 'VALID'))
    return inputs

@tf.contrib.framework.add_arg_scope
def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):
    """
    Pads the input along the spatial dimensions independently of input size.

    Args:
      inputs: A tensor of size [batch, channels, height_in, width_in] or
        [batch, height_in, width_in, channels] depending on data_format.
      kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                   Should be a positive integer.
      mode: The mode for tf.pad.

    Returns:
      A tensor with the same format as the input with the data either intact
      (if kernel_size == 1) or padded (if kernel_size > 1).
    """
    pad_total = kernel_size - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg

    padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                    [pad_beg, pad_end], [0, 0]], mode=mode)
    return padded_inputs

　　|-------yolov3.py #定义Darknet53，yolov3

import tensorflow as tf
from core import common
slim = tf.contrib.slim

class darknet53(object):
    """network for performing feature extraction"""

    def __init__(self, inputs):
        self.outputs = self.forward(inputs)

    def _darknet53_block(self, inputs, filters):
        """
        implement residuals block in darknet53
        """
        shortcut = inputs
        inputs = common._conv2d_fixed_padding(inputs, filters * 1, 1)
        inputs = common._conv2d_fixed_padding(inputs, filters * 2, 3)

        inputs = inputs + shortcut
        return inputs

    def forward(self, inputs):

        inputs = common._conv2d_fixed_padding(inputs, 32,  3, strides=1)
        inputs = common._conv2d_fixed_padding(inputs, 64,  3, strides=2)
        inputs = self._darknet53_block(inputs, 32)
        inputs = common._conv2d_fixed_padding(inputs, 128, 3, strides=2)

        for i in range(2):
            inputs = self._darknet53_block(inputs, 64)

        inputs = common._conv2d_fixed_padding(inputs, 256, 3, strides=2)

        for i in range(8):
            inputs = self._darknet53_block(inputs, 128)

        route_1 = inputs
        inputs = common._conv2d_fixed_padding(inputs, 512, 3, strides=2)

        for i in range(8):
            inputs = self._darknet53_block(inputs, 256)

        route_2 = inputs
        inputs = common._conv2d_fixed_padding(inputs, 1024, 3, strides=2)

        for i in range(4):
            inputs = self._darknet53_block(inputs, 512)

        return route_1, route_2, inputs

class yolov3(object):

    def __init__(self, num_classes, anchors,
                 batch_norm_decay=0.9, leaky_relu=0.1):

        # self._ANCHORS = [[10 ,13], [16 , 30], [33 , 23],
                         # [30 ,61], [62 , 45], [59 ,119],
                         # [116,90], [156,198], [373,326]]
        self._ANCHORS = anchors
        self._BATCH_NORM_DECAY = batch_norm_decay
        self._LEAKY_RELU = leaky_relu
        self._NUM_CLASSES = num_classes
        self.feature_maps = [] # [[None, 13, 13, 255], [None, 26, 26, 255], [None, 52, 52, 255]]

    def _yolo_block(self, inputs, filters):
        inputs = common._conv2d_fixed_padding(inputs, filters * 1, 1)
        inputs = common._conv2d_fixed_padding(inputs, filters * 2, 3)
        inputs = common._conv2d_fixed_padding(inputs, filters * 1, 1)
        inputs = common._conv2d_fixed_padding(inputs, filters * 2, 3)
        inputs = common._conv2d_fixed_padding(inputs, filters * 1, 1)
        route = inputs
        inputs = common._conv2d_fixed_padding(inputs, filters * 2, 3)
        return route, inputs

    def _detection_layer(self, inputs, anchors):
        num_anchors = len(anchors)
        feature_map = slim.conv2d(inputs, num_anchors * (5 + self._NUM_CLASSES), 1,
                                stride=1, normalizer_fn=None,
                                activation_fn=None,
                                biases_initializer=tf.zeros_initializer())
        return feature_map

    def _reorg_layer(self, feature_map, anchors):

        num_anchors = len(anchors) # num_anchors=3
        grid_size = feature_map.shape.as_list()[1:3]
        # the downscale image in height and weight
        stride = tf.cast(self.img_size // grid_size, tf.float32) # [h,w] -> [y,x]
        feature_map = tf.reshape(feature_map, [-1, grid_size[0], grid_size[1], num_anchors, 5 + self._NUM_CLASSES])

        box_centers, box_sizes, conf_logits, prob_logits = tf.split(
            feature_map, [2, 2, 1, self._NUM_CLASSES], axis=-1)

        box_centers = tf.nn.sigmoid(box_centers)

        grid_x = tf.range(grid_size[1], dtype=tf.int32)
        grid_y = tf.range(grid_size[0], dtype=tf.int32)

        a, b = tf.meshgrid(grid_x, grid_y)
        x_offset   = tf.reshape(a, (-1, 1))
        y_offset   = tf.reshape(b, (-1, 1))
        x_y_offset = tf.concat([x_offset, y_offset], axis=-1)
        x_y_offset = tf.reshape(x_y_offset, [grid_size[0], grid_size[1], 1, 2])
        x_y_offset = tf.cast(x_y_offset, tf.float32)

        box_centers = box_centers + x_y_offset
        box_centers = box_centers * stride[::-1]

        box_sizes = tf.exp(box_sizes) * anchors # anchors -> [w, h]
        boxes = tf.concat([box_centers, box_sizes], axis=-1)
        return x_y_offset, boxes, conf_logits, prob_logits

    @staticmethod
    def _upsample(inputs, out_shape):

        new_height, new_width = out_shape[1], out_shape[2]
        inputs = tf.image.resize_nearest_neighbor(inputs, (new_height, new_width))
        inputs = tf.identity(inputs, name='upsampled')

        return inputs

    # @staticmethod
    # def _upsample(inputs, out_shape):
        # """
        # replace resize_nearest_neighbor with conv2d_transpose To support TensorRT 5 optimization
        # """
        # new_height, new_width = out_shape[1], out_shape[2]
        # filters = 256 if (new_height == 26 and new_width==26) else 128
        # inputs = tf.layers.conv2d_transpose(inputs, filters, kernel_size=3, padding='same',
                                            # strides=(2,2), kernel_initializer=tf.ones_initializer())
        # return inputs

    def forward(self, inputs, is_training=False, reuse=False):
        """
        Creates YOLO v3 model.

        :param inputs: a 4-D tensor of size [batch_size, height, width, channels].
               Dimension batch_size may be undefined. The channel order is RGB.
        :param is_training: whether is training or not.
        :param reuse: whether or not the network and its variables should be reused.
        :return:
        """
        # it will be needed later on
        self.img_size = tf.shape(inputs)[1:3]
        # set batch norm params
        batch_norm_params = {
            'decay': self._BATCH_NORM_DECAY,
            'epsilon': 1e-05,
            'scale': True,
            'is_training': is_training,
            'fused': None,  # Use fused batch norm if possible.
        }

        # Set activation_fn and parameters for conv2d, batch_norm.
        with slim.arg_scope([slim.conv2d, slim.batch_norm, common._fixed_padding],reuse=reuse):
            with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm,
                                normalizer_params=batch_norm_params,
                                biases_initializer=None,
                                activation_fn=lambda x: tf.nn.leaky_relu(x, alpha=self._LEAKY_RELU)):
                with tf.variable_scope('darknet-53'):
                    route_1, route_2, inputs = darknet53(inputs).outputs

                with tf.variable_scope('yolo-v3'):
                    route, inputs = self._yolo_block(inputs, 512)
                    feature_map_1 = self._detection_layer(inputs, self._ANCHORS[6:9])
                    feature_map_1 = tf.identity(feature_map_1, name='feature_map_1')

                    inputs = common._conv2d_fixed_padding(route, 256, 1)
                    upsample_size = route_2.get_shape().as_list()
                    inputs = self._upsample(inputs, upsample_size)
                    inputs = tf.concat([inputs, route_2], axis=3)

                    route, inputs = self._yolo_block(inputs, 256)
                    feature_map_2 = self._detection_layer(inputs, self._ANCHORS[3:6])
                    feature_map_2 = tf.identity(feature_map_2, name='feature_map_2')

                    inputs = common._conv2d_fixed_padding(route, 128, 1)
                    upsample_size = route_1.get_shape().as_list()
                    inputs = self._upsample(inputs, upsample_size)
                    inputs = tf.concat([inputs, route_1], axis=3)

                    route, inputs = self._yolo_block(inputs, 128)
                    feature_map_3 = self._detection_layer(inputs, self._ANCHORS[0:3])
                    feature_map_3 = tf.identity(feature_map_3, name='feature_map_3')

            return feature_map_1, feature_map_2, feature_map_3

    def _reshape(self, x_y_offset, boxes, confs, probs):

        grid_size = x_y_offset.shape.as_list()[:2]
        boxes = tf.reshape(boxes, [-1, grid_size[0]*grid_size[1]*3, 4])
        confs = tf.reshape(confs, [-1, grid_size[0]*grid_size[1]*3, 1])
        probs = tf.reshape(probs, [-1, grid_size[0]*grid_size[1]*3, self._NUM_CLASSES])

        return boxes, confs, probs

    def predict(self, feature_maps):
        """
        Note: given by feature_maps, compute the receptive field
              and get boxes, confs and class_probs
        input_argument: feature_maps -> [None, 13, 13, 255],
                                        [None, 26, 26, 255],
                                        [None, 52, 52, 255],
        """
        feature_map_1, feature_map_2, feature_map_3 = feature_maps
        feature_map_anchors = [(feature_map_1, self._ANCHORS[6:9]),
                               (feature_map_2, self._ANCHORS[3:6]),
                               (feature_map_3, self._ANCHORS[0:3]),]

        results = [self._reorg_layer(feature_map, anchors) for (feature_map, anchors) in feature_map_anchors]
        boxes_list, confs_list, probs_list = [], [], []

        for result in results:
            boxes, conf_logits, prob_logits = self._reshape(*result)

            confs = tf.sigmoid(conf_logits)
            probs = tf.sigmoid(prob_logits)

            boxes_list.append(boxes)
            confs_list.append(confs)
            probs_list.append(probs)

        boxes = tf.concat(boxes_list, axis=1)
        confs = tf.concat(confs_list, axis=1)
        probs = tf.concat(probs_list, axis=1)

        center_x, center_y, width, height = tf.split(boxes, [1,1,1,1], axis=-1)
        x0 = center_x - width   / 2.
        y0 = center_y - height  / 2.
        x1 = center_x + width   / 2.
        y1 = center_y + height  / 2.

        boxes = tf.concat([x0, y0, x1, y1], axis=-1)
        return boxes, confs, probs

    def compute_loss(self, pred_feature_map, y_true, ignore_thresh=0.5, max_box_per_image=8):
        """
        Note: compute the loss
        Arguments: y_pred, list -> [feature_map_1, feature_map_2, feature_map_3]
                                        the shape of [None, 13, 13, 3*85]. etc
        """
        loss_xy, loss_wh, loss_conf, loss_class = 0., 0., 0., 0.
        total_loss = 0.
        # total_loss, rec_50, rec_75,  avg_iou    = 0., 0., 0., 0.
        _ANCHORS = [self._ANCHORS[6:9], self._ANCHORS[3:6], self._ANCHORS[0:3]]

        for i in range(len(pred_feature_map)):
            result = self.loss_layer(pred_feature_map[i], y_true[i], _ANCHORS[i])
            loss_xy    += result[0]
            loss_wh    += result[1]
            loss_conf  += result[2]
            loss_class += result[3]

        total_loss = loss_xy + loss_wh + loss_conf + loss_class
        return [total_loss, loss_xy, loss_wh, loss_conf, loss_class]

    def loss_layer(self, feature_map_i, y_true, anchors):
        # size in [h, w] format! don't get messed up!
        grid_size = tf.shape(feature_map_i)[1:3]
        grid_size_ = feature_map_i.shape.as_list()[1:3]

        y_true = tf.reshape(y_true, [-1, grid_size_[0], grid_size_[1], 3, 5+self._NUM_CLASSES])

        # the downscale ratio in height and weight
        ratio = tf.cast(self.img_size / grid_size, tf.float32)
        # N: batch_size
        N = tf.cast(tf.shape(feature_map_i)[0], tf.float32)

        x_y_offset, pred_boxes, pred_conf_logits, pred_prob_logits = self._reorg_layer(feature_map_i, anchors)
        # shape: take 416x416 input image and 13*13 feature_map for example:
        # [N, 13, 13, 3, 1]
        object_mask = y_true[..., 4:5]
        # shape: [N, 13, 13, 3, 4] & [N, 13, 13, 3] ==> [V, 4]
        # V: num of true gt box
        valid_true_boxes = tf.boolean_mask(y_true[..., 0:4], tf.cast(object_mask[..., 0], 'bool'))

        # shape: [V, 2]
        valid_true_box_xy = valid_true_boxes[:, 0:2]
        valid_true_box_wh = valid_true_boxes[:, 2:4]
        # shape: [N, 13, 13, 3, 2]
        pred_box_xy = pred_boxes[..., 0:2]
        pred_box_wh = pred_boxes[..., 2:4]

        # calc iou
        # shape: [N, 13, 13, 3, V]
        iou = self._broadcast_iou(valid_true_box_xy, valid_true_box_wh, pred_box_xy, pred_box_wh)

        # shape: [N, 13, 13, 3]
        best_iou = tf.reduce_max(iou, axis=-1)
        # get_ignore_mask
        ignore_mask = tf.cast(best_iou < 0.5, tf.float32)
        # shape: [N, 13, 13, 3, 1]
        ignore_mask = tf.expand_dims(ignore_mask, -1)
        # get xy coordinates in one cell from the feature_map
        # numerical range: 0 ~ 1
        # shape: [N, 13, 13, 3, 2]
        true_xy = y_true[..., 0:2] / ratio[::-1] - x_y_offset
        pred_xy = pred_box_xy      / ratio[::-1] - x_y_offset

        # get_tw_th, numerical range: 0 ~ 1
        # shape: [N, 13, 13, 3, 2]
        true_tw_th = y_true[..., 2:4] / anchors
        pred_tw_th = pred_box_wh      / anchors
        # for numerical stability
        true_tw_th = tf.where(condition=tf.equal(true_tw_th, 0),
                              x=tf.ones_like(true_tw_th), y=true_tw_th)
        pred_tw_th = tf.where(condition=tf.equal(pred_tw_th, 0),
                              x=tf.ones_like(pred_tw_th), y=pred_tw_th)

        true_tw_th = tf.log(tf.clip_by_value(true_tw_th, 1e-9, 1e9))
        pred_tw_th = tf.log(tf.clip_by_value(pred_tw_th, 1e-9, 1e9))

        # box size punishment:
        # box with smaller area has bigger weight. This is taken from the yolo darknet C source code.
        # shape: [N, 13, 13, 3, 1]
        #why use box_loss_scale?
        box_loss_scale = 2. - (y_true[..., 2:3] / tf.cast(self.img_size[1], tf.float32)) * (y_true[..., 3:4] / tf.cast(self.img_size[0], tf.float32))

        # shape: [N, 13, 13, 3, 1]
        xy_loss = tf.reduce_sum(tf.square(true_xy    - pred_xy) * object_mask * box_loss_scale) / N
        wh_loss = tf.reduce_sum(tf.square(true_tw_th - pred_tw_th) * object_mask * box_loss_scale) / N

        # shape: [N, 13, 13, 3, 1]
        conf_pos_mask = object_mask
        conf_neg_mask = (1 - object_mask) * ignore_mask
        conf_loss_pos = conf_pos_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)
        conf_loss_neg = conf_neg_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)
        conf_loss = tf.reduce_sum(conf_loss_pos + conf_loss_neg) / N

        # shape: [N, 13, 13, 3, 1]
        class_loss = object_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true[..., 5:], logits=pred_prob_logits)
        class_loss = tf.reduce_sum(class_loss) / N

        return xy_loss, wh_loss, conf_loss, class_loss

    def _broadcast_iou(self, true_box_xy, true_box_wh, pred_box_xy, pred_box_wh):
        '''
        maintain an efficient way to calculate the ios matrix between ground truth true boxes and the predicted boxes
        note: here we only care about the size match
        '''
        # shape:
        # true_box_??: [V, 2]
        # pred_box_??: [N, 13, 13, 3, 2]    

        # shape: [N, 13, 13, 3, 1, 2]
        pred_box_xy = tf.expand_dims(pred_box_xy, -2)
        pred_box_wh = tf.expand_dims(pred_box_wh, -2)

        # shape: [1, V, 2]
        true_box_xy = tf.expand_dims(true_box_xy, 0)
        true_box_wh = tf.expand_dims(true_box_wh, 0)

        # [N, 13, 13, 3, 1, 2] & [1, V, 2] ==> [N, 13, 13, 3, V, 2]
        intersect_mins = tf.maximum(pred_box_xy - pred_box_wh / 2.,
                                    true_box_xy - true_box_wh / 2.)
        intersect_maxs = tf.minimum(pred_box_xy + pred_box_wh / 2.,
                                    true_box_xy + true_box_wh / 2.)
        intersect_wh = tf.maximum(intersect_maxs - intersect_mins, 0.)

        # shape: [N, 13, 13, 3, V]
        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
        # shape: [N, 13, 13, 3, 1]
        pred_box_area  = pred_box_wh[..., 0]  * pred_box_wh[..., 1]
        # shape: [1, V]
        true_box_area  = true_box_wh[..., 0]  * true_box_wh[..., 1]
        # [N, 13, 13, 3, V]
        iou = intersect_area / (pred_box_area + true_box_area - intersect_area)

        return iou

　　|-------convert_weight.py  将darknet(c版)yolov3权重转换成tensorflow版pb

import os
import sys
import wget
import time
import argparse
import tensorflow as tf
from core import yolov3, utils

class parser(argparse.ArgumentParser):

    def __init__(self,description):
        super(parser, self).__init__(description)

        self.add_argument(
            "--ckpt_file", "-cf", default='./checkpoint/yolov3.ckpt', type=str,
            help="[default: %(default)s] The checkpoint file ...",
            metavar="<CF>",
        )

        self.add_argument(
            "--num_classes", "-nc", default=80, type=int,
            help="[default: %(default)s] The number of classes ...",
            metavar="<NC>",
        )

        self.add_argument(
            "--anchors_path", "-ap", default="./data/coco_anchors.txt", type=str,
            help="[default: %(default)s] The path of anchors ...",
            metavar="<AP>",
        )

        self.add_argument(
            "--weights_path", "-wp", default='./checkpoint/yolov3.weights', type=str,
            help="[default: %(default)s] Download binary file with desired weights",
            metavar="<WP>",
        )

        self.add_argument(
            "--convert", "-cv", action='store_true',
            help="[default: %(default)s] Downloading yolov3 weights and convert them",
        )

        self.add_argument(
            "--freeze", "-fz", action='store_true',
            help="[default: %(default)s] freeze the yolov3 graph to pb ...",
        )

        self.add_argument(
            "--image_h", "-ih", default=416, type=int,
            help="[default: %(default)s] The height of image, 416 or 608",
            metavar="<IH>",
        )

        self.add_argument(
            "--image_w", "-iw", default=416, type=int,
            help="[default: %(default)s] The width of image, 416 or 608",
            metavar="<IW>",
        )

        self.add_argument(
            "--iou_threshold", "-it", default=0.5, type=float,
            help="[default: %(default)s] The iou_threshold for gpu nms",
            metavar="<IT>",
        )

        self.add_argument(
            "--score_threshold", "-st", default=0.5, type=float,
            help="[default: %(default)s] The score_threshold for gpu nms",
            metavar="<ST>",
        )

def main(argv):

    flags = parser(description="freeze yolov3 graph from checkpoint file").parse_args()
    print("=> the input image size is [%d, %d]" %(flags.image_h, flags.image_w))
    anchors = utils.get_anchors(flags.anchors_path, flags.image_h, flags.image_w)
    model = yolov3.yolov3(flags.num_classes, anchors)

    with tf.Graph().as_default() as graph:
        sess = tf.Session(graph=graph)
        inputs = tf.placeholder(tf.float32, [1, flags.image_h, flags.image_w, 3]) # placeholder for detector inputs
        print("=>", inputs)

        with tf.variable_scope('yolov3'):
            feature_map = model.forward(inputs, is_training=False)

        boxes, confs, probs = model.predict(feature_map)
        scores = confs * probs
        print("=>", boxes.name[:-2], scores.name[:-2])
        cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
        boxes, scores, labels = utils.gpu_nms(boxes, scores, flags.num_classes,
                                              score_thresh=flags.score_threshold,
                                              iou_thresh=flags.iou_threshold)
        print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
        gpu_out_node_names = [boxes.name[:-2], scores.name[:-2], labels.name[:-2]]
        feature_map_1, feature_map_2, feature_map_3 = feature_map
        saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))

        if flags.convert:
            if not os.path.exists(flags.weights_path):
                url = 'https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3.weights'
                for i in range(3):
                    time.sleep(1)
                    print("=> %s does not exists ! " %flags.weights_path)
                print("=> It will take a while to download it from %s" %url)
                print('=> Downloading yolov3 weights ... ')
                wget.download(url, flags.weights_path)

            load_ops = utils.load_weights(tf.global_variables(scope='yolov3'), flags.weights_path)
            sess.run(load_ops)
            save_path = saver.save(sess, save_path=flags.ckpt_file)
            print('=> model saved in path: {}'.format(save_path))

        if flags.freeze:
            saver.restore(sess, flags.ckpt_file)
            print('=> checkpoint file restored from ', flags.ckpt_file)
            utils.freeze_graph(sess, './checkpoint/yolov3_cpu_nms.pb', cpu_out_node_names)
            utils.freeze_graph(sess, './checkpoint/yolov3_gpu_nms.pb', gpu_out_node_names)

if __name__ == "__main__": main(sys.argv)

　　|------yolov3_train.py #加载参数，数据，训练

import tensorflow as tf
from core import utils, yolov3
from core.dataset import dataset, Parser
sess = tf.Session()

IMAGE_H, IMAGE_W = 416, 416
BATCH_SIZE       = 8
STEPS            = 2500
LR               = 0.001 # if Nan, set 0.0005, 0.0001
DECAY_STEPS      = 100
DECAY_RATE       = 0.9
SHUFFLE_SIZE     = 200
CLASSES          = utils.read_coco_names('./data/raccoon.names')
ANCHORS          = utils.get_anchors('./data/raccoon_anchors.txt', IMAGE_H, IMAGE_W)
NUM_CLASSES      = len(CLASSES)
EVAL_INTERNAL    = 100
SAVE_INTERNAL    = 500

train_tfrecord   = "./raccoon_dataset/raccoon_train.tfrecords"
test_tfrecord    = "./raccoon_dataset/raccoon_test.tfrecords"

parser   = Parser(IMAGE_H, IMAGE_W, ANCHORS, NUM_CLASSES)
trainset = dataset(parser, train_tfrecord, BATCH_SIZE, shuffle=SHUFFLE_SIZE)
testset  = dataset(parser, test_tfrecord , BATCH_SIZE, shuffle=None)

is_training = tf.placeholder(tf.bool)
example = tf.cond(is_training, lambda: trainset.get_next(), lambda: testset.get_next())

images, *y_true = example
model = yolov3.yolov3(NUM_CLASSES, ANCHORS)

with tf.variable_scope('yolov3'):
    pred_feature_map = model.forward(images, is_training=is_training)
    loss             = model.compute_loss(pred_feature_map, y_true)
    y_pred           = model.predict(pred_feature_map)

tf.summary.scalar("loss/coord_loss",   loss[1])
tf.summary.scalar("loss/sizes_loss",   loss[2])

tf.summary.scalar("loss/confs_loss",   loss[3])
tf.summary.scalar("loss/class_loss",   loss[4])

global_step = tf.Variable(0, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
write_op = tf.summary.merge_all()
writer_train = tf.summary.FileWriter("./data/train")
writer_test  = tf.summary.FileWriter("./data/test")

saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=["yolov3/darknet-53"]))
update_vars = tf.contrib.framework.get_variables_to_restore(include=["yolov3/yolo-v3"])
learning_rate = tf.train.exponential_decay(LR, global_step, decay_steps=DECAY_STEPS, decay_rate=DECAY_RATE, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate)

# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
    train_op = optimizer.minimize(loss[0], var_list=update_vars, global_step=global_step)

sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
saver_to_restore.restore(sess, "./checkpoint/yolov3.ckpt")
saver = tf.train.Saver(max_to_keep=2)

for step in range(STEPS):
    run_items = sess.run([train_op, write_op, y_pred, y_true] + loss, feed_dict={is_training:True})

    if (step+1) % EVAL_INTERNAL == 0:
        train_rec_value, train_prec_value = utils.evaluate(run_items[2], run_items[3])

    writer_train.add_summary(run_items[1], global_step=step)
    writer_train.flush() # Flushes the event file to disk
    if (step+1) % SAVE_INTERNAL == 0:
          saver.save(sess, save_path="./checkpoint/yolov3.ckpt", global_step=step+1)

    print("=> STEP %10d [TRAIN]:\tloss_xy:%7.4f \tloss_wh:%7.4f \tloss_conf:%7.4f \tloss_class:%7.4f"
        %(step+1, run_items[5], run_items[6], run_items[7], run_items[8]))

    run_items = sess.run([write_op, y_pred, y_true] + loss, feed_dict={is_training:False})
    if (step+1) % EVAL_INTERNAL == 0:
        test_rec_value, test_prec_value = utils.evaluate(run_items[1], run_items[2])
        print("\n=======================> evaluation result <================================\n")
        print("=> STEP %10d [TRAIN]:\trecall:%7.4f \tprecision:%7.4f" %(step+1, train_rec_value, train_prec_value))
        print("=> STEP %10d [VALID]:\trecall:%7.4f \tprecision:%7.4f" %(step+1, test_rec_value,  test_prec_value))
        print("\n=======================> evaluation result <================================\n")

    writer_test.add_summary(run_items[0], global_step=step)
    writer_test.flush() # Flushes the event file to disk

　　|------evaluate.py # 评估模型效果

import sys
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from PIL import Image
from core import utils, yolov3
from core.dataset import dataset, Parser
sess = tf.Session()

IMAGE_H, IMAGE_W = 416, 416
CLASSES          = utils.read_coco_names('./data/raccoon.names')
NUM_CLASSES      = len(CLASSES)
ANCHORS          = utils.get_anchors('./data/raccoon_anchors.txt', IMAGE_H, IMAGE_W)
CKPT_FILE        = "./checkpoint/yolov3.ckpt-2500"
IOU_THRESH       = 0.5
SCORE_THRESH     = 0.3

all_detections   = []
all_annotations  = []
all_aver_precs   = {CLASSES[i]:0. for i in range(NUM_CLASSES)}

test_tfrecord    = "./raccoon_dataset/raccoon_*.tfrecords"
parser           = Parser(IMAGE_H, IMAGE_W, ANCHORS, NUM_CLASSES)
testset          = dataset(parser, test_tfrecord , batch_size=1, shuffle=None, repeat=False)

images_tensor, *y_true_tensor  = testset.get_next()
model = yolov3.yolov3(NUM_CLASSES, ANCHORS)
with tf.variable_scope('yolov3'):
    pred_feature_map    = model.forward(images_tensor, is_training=False)
    y_pred_tensor       = model.predict(pred_feature_map)

saver = tf.train.Saver()
saver.restore(sess, CKPT_FILE)

try:
    image_idx = 0
    while True:
        y_pred, y_true, image  = sess.run([y_pred_tensor, y_true_tensor, images_tensor])
        pred_boxes = y_pred[0][0]
        pred_confs = y_pred[1][0]
        pred_probs = y_pred[2][0]
        image      = Image.fromarray(np.uint8(image[0]*255))

        true_labels_list, true_boxes_list = [], []
        for i in range(3):
            true_probs_temp = y_true[i][..., 5: ]
            true_boxes_temp = y_true[i][..., 0:4]
            object_mask     = true_probs_temp.sum(axis=-1) > 0

            true_probs_temp = true_probs_temp[object_mask]
            true_boxes_temp = true_boxes_temp[object_mask]

            true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()
            true_boxes_list  += true_boxes_temp.tolist()

        pred_boxes, pred_scores, pred_labels = utils.cpu_nms(pred_boxes, pred_confs*pred_probs, NUM_CLASSES,
                                                      score_thresh=SCORE_THRESH, iou_thresh=IOU_THRESH)
        # image = utils.draw_boxes(image, pred_boxes, pred_scores, pred_labels, CLASSES, [IMAGE_H, IMAGE_W], show=True)
        true_boxes = np.array(true_boxes_list)
        box_centers, box_sizes = true_boxes[:,0:2], true_boxes[:,2:4]

        true_boxes[:,0:2] = box_centers - box_sizes / 2.
        true_boxes[:,2:4] = true_boxes[:,0:2] + box_sizes
        pred_labels_list = [] if pred_labels is None else pred_labels.tolist()

        all_detections.append( [pred_boxes, pred_scores, pred_labels_list])
        all_annotations.append([true_boxes, true_labels_list])
        image_idx += 1
        if image_idx % 100 == 0:
            sys.stdout.write(".")
            sys.stdout.flush()

except tf.errors.OutOfRangeError:
    pass

for idx in range(NUM_CLASSES):
    true_positives  = []
    scores = []
    num_annotations = 0

    for i in tqdm(range(len(all_annotations)), desc="Computing AP for class %12s" %(CLASSES[idx])):
        pred_boxes, pred_scores, pred_labels_list = all_detections[i]
        true_boxes, true_labels_list              = all_annotations[i]
        detected                                  = []
        num_annotations                          += true_labels_list.count(idx)

        for k in range(len(pred_labels_list)):
            if pred_labels_list[k] != idx: continue

            scores.append(pred_scores[k])
            ious = utils.bbox_iou(pred_boxes[k:k+1], true_boxes)
            m    = np.argmax(ious)
            if ious[m] > IOU_THRESH and pred_labels_list[k] == true_labels_list[m] and m not in detected:
                detected.append(m)
                true_positives.append(1)
            else:
                true_positives.append(0)

    num_predictions = len(true_positives)
    true_positives  = np.array(true_positives)
    false_positives = np.ones_like(true_positives) - true_positives
    # sorted by score
    indices = np.argsort(-np.array(scores))
    false_positives = false_positives[indices]
    true_positives = true_positives[indices]
    # compute false positives and true positives
    false_positives = np.cumsum(false_positives)
    true_positives = np.cumsum(true_positives)
    # compute recall and precision
    recall    = true_positives / np.maximum(num_annotations, np.finfo(np.float64).eps)
    precision = true_positives / np.maximum(num_predictions, np.finfo(np.float64).eps)
    # compute average precision
    average_precision = utils.compute_ap(recall, precision)
    all_aver_precs[CLASSES[idx]] = average_precision

for idx in range(NUM_CLASSES):
    cls_name = CLASSES[idx]
    print("=> Class %10s - AP: %.4f" %(cls_name, all_aver_precs[cls_name]))

print("=> mAP: %.4f" %(sum(all_aver_precs.values()) / NUM_CLASSES))

　　|------yolov3.py # 使用训练好的模型推理

import numpy as np
import tensorflow as tf
from PIL import Image
from core import utils

IMAGE_H, IMAGE_W = 416, 416
classes = utils.read_coco_names('./data/raccoon.names')
num_classes = len(classes)
image_path = "./raccoon_dataset/images/raccoon-182.jpg"  # 181,
img = Image.open(image_path)
img_resized = np.array(img.resize(size=(IMAGE_W, IMAGE_H)), dtype=np.float32)
img_resized = img_resized / 255.
cpu_nms_graph = tf.Graph()

input_tensor, output_tensors = utils.read_pb_return_tensors(cpu_nms_graph, "./checkpoint/yolov3_cpu_nms.pb",
                                           ["Placeholder:0", "concat_9:0", "mul_6:0"])
with tf.Session(graph=cpu_nms_graph) as sess:
    boxes, scores = sess.run(output_tensors, feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
    boxes, scores, labels = utils.cpu_nms(boxes, scores, num_classes, score_thresh=0.3, iou_thresh=0.5)
    image = utils.draw_boxes(img, boxes, scores, labels, classes, [IMAGE_H, IMAGE_W], show=True)

　　|------show_input_image.py #显示输入图片及BBOX

import cv2
import numpy as np
import tensorflow as tf
from core import utils
from PIL import Image
from core.dataset import Parser, dataset
sess = tf.Session()

IMAGE_H, IMAGE_W = 416, 416
BATCH_SIZE = 1
SHUFFLE_SIZE = 1

train_tfrecord = "./raccoon_dataset/raccoon_*.tfrecords"
anchors        = utils.get_anchors('./data/raccoon_anchors.txt', IMAGE_H, IMAGE_W)
classes = utils.read_coco_names('./data/raccoon.names')
num_classes = len(classes)

parser   = Parser(IMAGE_H, IMAGE_W, anchors, num_classes, debug=True)
trainset = dataset(parser, train_tfrecord, BATCH_SIZE, shuffle=SHUFFLE_SIZE)

is_training = tf.placeholder(tf.bool)
example = trainset.get_next()

for l in range(10):
    image, boxes = sess.run(example)
    image, boxes = image[0], boxes[0]

    n_box = len(boxes)
    for i in range(n_box):
        image = cv2.rectangle(image,(int(float(boxes[i][0])),
                                     int(float(boxes[i][1]))),
                                    (int(float(boxes[i][2])),
                                     int(float(boxes[i][3]))), (255,0,0), 1)
        label = classes[boxes[i][4]]
        image = cv2.putText(image, label, (int(float(boxes[i][0])),int(float(boxes[i][1]))),
                            cv2.FONT_HERSHEY_SIMPLEX,  .6, (0, 255, 0), 1, 2)

    image = Image.fromarray(np.uint8(image))
    image.show()

　　|------kmeans.py #计算输入数据中anchors的数据

import cv2
import argparse
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
current_palette = list(sns.xkcd_rgb.values())

def iou(box, clusters):
    """
    Calculates the Intersection over Union (IoU) between a box and k clusters.
    param:
        box: tuple or array, shifted to the origin (i. e. width and height)
        clusters: numpy array of shape (k, 2) where k is the number of clusters
    return:
        numpy array of shape (k, 0) where k is the number of clusters
    """
    x = np.minimum(clusters[:, 0], box[0])
    y = np.minimum(clusters[:, 1], box[1])
    if np.count_nonzero(x == 0) > 0 or np.count_nonzero(y == 0) > 0:
        raise ValueError("Box has no area")

    intersection = x * y
    box_area = box[0] * box[1]
    cluster_area = clusters[:, 0] * clusters[:, 1]

    iou_ = intersection / (box_area + cluster_area - intersection)

    return iou_

def kmeans(boxes, k, dist=np.median,seed=1):
    """
    Calculates k-means clustering with the Intersection over Union (IoU) metric.
    :param boxes: numpy array of shape (r, 2), where r is the number of rows
    :param k: number of clusters
    :param dist: distance function
    :return: numpy array of shape (k, 2)
    """
    rows = boxes.shape[0]
    distances     = np.empty((rows, k)) ## N row x N cluster
    last_clusters = np.zeros((rows,))

    np.random.seed(seed)

    # initialize the cluster centers to be k items
    clusters = boxes[np.random.choice(rows, k, replace=False)]

    while True:
        # Step 1: allocate each item to the closest cluster centers
        for icluster in range(k): # I made change to lars76's code here to make the code faster
            distances[:,icluster] = 1 - iou(clusters[icluster], boxes)

        nearest_clusters = np.argmin(distances, axis=1)

        if (last_clusters == nearest_clusters).all():
            break

        # Step 2: calculate the cluster centers as mean (or median) of all the cases in the clusters.
        for cluster in range(k):
            clusters[cluster] = dist(boxes[nearest_clusters == cluster], axis=0)
        last_clusters = nearest_clusters

    return clusters, nearest_clusters, distances

def parse_anno(annotation_path):
    anno = open(annotation_path, 'r')
    result = []
    for line in anno:
        s = line.strip().split(' ')
        image = cv2.imread(s[0])
        image_h, image_w = image.shape[:2]
        s = s[1:]
        box_cnt = len(s) // 5
        for i in range(box_cnt):
            x_min, y_min, x_max, y_max = float(s[i*5+0]), float(s[i*5+1]), float(s[i*5+2]), float(s[i*5+3])
            width  = (x_max - x_min) / image_w
            height = (y_max - y_min) / image_h
            result.append([width, height])
    result = np.asarray(result)
    return result

def plot_cluster_result(clusters,nearest_clusters,WithinClusterSumDist,wh,k):
    for icluster in np.unique(nearest_clusters):
        pick = nearest_clusters==icluster
        c = current_palette[icluster]
        plt.rc('font', size=8)
        plt.plot(wh[pick,0],wh[pick,1],"p",
                 color=c,
                 alpha=0.5,label="cluster = {}, N = {:6.0f}".format(icluster,np.sum(pick)))
        plt.text(clusters[icluster,0],
                 clusters[icluster,1],
                 "c{}".format(icluster),
                 fontsize=20,color="red")
        plt.title("Clusters=%d" %k)
        plt.xlabel("width")
        plt.ylabel("height")
    plt.legend(title="Mean IoU = {:5.4f}".format(WithinClusterSumDist))
    plt.tight_layout()
    plt.savefig("./kmeans.jpg")
    plt.show()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset_txt", type=str, default="./raccoon_dataset/train.txt")
    parser.add_argument("--anchors_txt", type=str, default="./data/raccoon_anchors.txt")
    parser.add_argument("--cluster_num", type=int, default=9)
    args = parser.parse_args()
    anno_result = parse_anno(args.dataset_txt)
    clusters, nearest_clusters, distances = kmeans(anno_result, args.cluster_num)

    # sorted by area
    area = clusters[:, 0] * clusters[:, 1]
    indice = np.argsort(area)
    clusters = clusters[indice]
    with open(args.anchors_txt, "w") as f:
        for i in range(args.cluster_num):
            width, height = clusters[i]
            f.writelines(str(width) + " " + str(height) + " ")

    WithinClusterMeanDist = np.mean(distances[np.arange(distances.shape[0]),nearest_clusters])
    plot_cluster_result(clusters, nearest_clusters, 1-WithinClusterMeanDist, anno_result, args.cluster_num)

　　|------test_video.py #视频方式测试模型效果

import cv2
import time
import numpy as np
import tensorflow as tf
from PIL import Image
from core import utils

IMAGE_H, IMAGE_W = 416, 416
video_path = "./data/demo_data/road.mp4"
video_path = 0 # use camera
classes = utils.read_coco_names('./data/coco.names')
num_classes = len(classes)
input_tensor, output_tensors = utils.read_pb_return_tensors(tf.get_default_graph(),
                                                            "./checkpoint/yolov3_cpu_nms.pb",
                                                            ["Placeholder:0", "concat_9:0", "mul_6:0"])
with tf.Session() as sess:
    vid = cv2.VideoCapture(video_path)
    while True:
        return_value, frame = vid.read()
        if return_value:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            image = Image.fromarray(frame)
        else:
            raise ValueError("No image!")
        img_resized = np.array(image.resize(size=(IMAGE_H, IMAGE_W)), dtype=np.float32)
        img_resized = img_resized / 255.
        prev_time = time.time()

        boxes, scores = sess.run(output_tensors, feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
        boxes, scores, labels = utils.cpu_nms(boxes, scores, num_classes, score_thresh=0.4, iou_thresh=0.5)
        image = utils.draw_boxes(image, boxes, scores, labels, classes, (IMAGE_H, IMAGE_W), show=False)

        curr_time = time.time()
        exec_time = curr_time - prev_time
        result = np.asarray(image)
        info = "time: %.2f ms" %(1000*exec_time)
        cv2.putText(result, text=info, org=(50, 70), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=1, color=(255, 0, 0), thickness=2)
        cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
        cv2.imshow("result", result)
        if cv2.waitKey(1) & 0xFF == ord('q'): break

三、 Yolov3-tiny

由于YOLOV3的模型过于庞大，无法用于ARM芯片。所以，这里使用了Yolov3-tiny。

配置文件修改：

首先，我们需要对yolov3-tiny.cfg进行编辑，
在这个文件里，我们需要关注[yolo]和[yolo]前的一个[convolutional]
首先将所有[yolo]里面classes修改为需要识别的类别数
得到classes=4
classes的含义是有多少种需要被识别的物体，这里我训练yolo识别4种物体，所以设置为4

对所有[yolo]的前一个[convolutional]中的filters进行修改
其取值为filters = 3 * ( classes + 5 ),由于上一步中classes=4所以这里filters取27
到这里，yolov3-tiny.cfg就修改完毕了
然后是修改model_data中的car_classes.txt，将待检测物体的标签填写进去，每种标签占一行。