Keras版Faster-RCNN代码学习（IOU，RPN）1

Posted 2021-01-02 kekexuanxaun

最近开始使用Keras来做深度学习，发现模型搭建相较于MXnet, Caffe等确实比较方便，适合于新手练手，于是找来了目标检测经典的模型Faster-RCNN的keras代码来练练手，代码的主题部分转自知乎专栏Learning Machine，作者张潇捷，链接如下： 
keras版faster-rcnn算法详解（1.RPN计算） 
keras版faster-rcnn算法详解 (2.roi计算及其他)

我再对代码中loss的计算，config的设置等细节进行学习 
Keras版Faster-RCNN代码学习（IOU，RPN）1 
Keras版Faster-RCNN代码学习（Batch Normalization）2 
Keras版Faster-RCNN代码学习（loss，xml解析）3 
Keras版Faster-RCNN代码学习（roipooling resnet/vgg）4 
Keras版Faster-RCNN代码学习（measure_map，train/test）5

config.py

from keras import backend as K
import math

class Config:

    def __init__(self):

        self.verbose = True

        self.network = ‘resnet50‘

        # setting for data augmentation
        self.use_horizontal_flips = False
        self.use_vertical_flips = False
        self.rot_90 = False

        # anchor box scales
        self.anchor_box_scales = [128, 256, 512]

        # anchor box ratios
        self.anchor_box_ratios = [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]]

        # size to resize the smallest side of the image
        self.im_size = 600

        # image channel-wise mean to subtract
        self.img_channel_mean = [103.939, 116.779, 123.68]
        self.img_scaling_factor = 1.0

        # number of ROIs at once
        self.num_rois = 4

        # stride at the RPN (this depends on the network configuration)
        self.rpn_stride = 16

        self.balanced_classes = False

        # scaling the stdev
        self.std_scaling = 4.0
        self.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]

        # overlaps for RPN
        self.rpn_min_overlap = 0.3
        self.rpn_max_overlap = 0.7

        # overlaps for classifier ROIs
        self.classifier_min_overlap = 0.1
        self.classifier_max_overlap = 0.5

        # placeholder for the class mapping, automatically generated by the parser
        self.class_mapping = None

        #location of pretrained weights for the base network 
        # weight files can be found at:
        # https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5
        # https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

        self.model_path = ‘model_frcnn.vgg.hdf5‘

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对代码所需要的参数进行配置

data_generators.py

import cv2
import numpy as np
import copy

#传递图像参数，增广配置参数，是否进行图像增广
def augment(img_data, config, augment=True):
    assert ‘filepath‘ in img_data
    assert ‘bboxes‘ in img_data
    assert ‘width‘ in img_data
    assert ‘height‘ in img_data

    img_data_aug = copy.deepcopy(img_data)

    img = cv2.imread(img_data_aug[‘filepath‘])

    if augment:
        rows, cols = img.shape[:2]
        #图像水平翻转，对应的bbox的对角坐标也进行水平翻转，翻转概率为50%
        if config.use_horizontal_flips and np.random.randint(0, 2) == 0:
            img = cv2.flip(img, 1)
            for bbox in img_data_aug[‘bboxes‘]:
                x1 = bbox[‘x1‘]
                x2 = bbox[‘x2‘]
                bbox[‘x2‘] = cols - x1
                bbox[‘x1‘] = cols - x2
        #图像垂直翻转，对应的bbox的对角坐标也进行垂直翻转，翻转概率为50%
        if config.use_vertical_flips and np.random.randint(0, 2) == 0:
            img = cv2.flip(img, 0)
            for bbox in img_data_aug[‘bboxes‘]:
                y1 = bbox[‘y1‘]
                y2 = bbox[‘y2‘]
                bbox[‘y2‘] = rows - y1
                bbox[‘y1‘] = rows - y2
        #图像按90度旋转，对应的bbox的对角坐标也进行90度旋转，旋转概率为50%
        if config.rot_90:
            angle = np.random.choice([0,90,180,270],1)[0]
            if angle == 270:
                img = np.transpose(img, (1,0,2))
                img = cv2.flip(img, 0)
            elif angle == 180:
                img = cv2.flip(img, -1)
            elif angle == 90:
                img = np.transpose(img, (1,0,2))
                img = cv2.flip(img, 1)
            elif angle == 0:
                pass

            for bbox in img_data_aug[‘bboxes‘]:
                x1 = bbox[‘x1‘]
                x2 = bbox[‘x2‘]
                y1 = bbox[‘y1‘]
                y2 = bbox[‘y2‘]
                if angle == 270:
                    bbox[‘x1‘] = y1
                    bbox[‘x2‘] = y2
                    bbox[‘y1‘] = cols - x2
                    bbox[‘y2‘] = cols - x1
                elif angle == 180:
                    bbox[‘x2‘] = cols - x1
                    bbox[‘x1‘] = cols - x2
                    bbox[‘y2‘] = rows - y1
                    bbox[‘y1‘] = rows - y2
                elif angle == 90:
                    bbox[‘x1‘] = rows - y2
                    bbox[‘x2‘] = rows - y1
                    bbox[‘y1‘] = x1
                    bbox[‘y2‘] = x2        
                elif angle == 0:
                    pass

    img_data_aug[‘width‘] = img.shape[1]
    img_data_aug[‘height‘] = img.shape[0]
    return img_data_aug, img

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关于坐标计算，还是有点绕的，可通过矩阵计算或者画图描述（画图描述比较清晰，对应对角坐标会发生变化，对角坐标为一个最小的，一个最大的）如[x1,y1,x2,y2]，则x1 < x2,y1 < y2，面积为(x2-x1)(y2-y1)，所以对角坐标翻转后的坐标并不是对角坐标，需要调整，即找到最小的x,y和最大的x,y。如[x1,y1,x2,y2]，则x1 < x2,y1 < y2，进行水平翻转后，cols - x2 < cols - x1,y1 < y2,重新组合的坐标为[cols - x2,y1,cols - x1,y2],其他同理。

IOU，RPN计算

from __future__ import absolute_import
import numpy as np
import cv2
import random
import copy
from . import data_augment
import threading
import itertools

#并集
def union(au, bu, area_intersection):
    area_a = (au[2] - au[0]) * (au[3] - au[1])
    area_b = (bu[2] - bu[0]) * (bu[3] - bu[1])
    area_union = area_a + area_b - area_intersection
    return area_union

#交集
def intersection(ai, bi):
    x = max(ai[0], bi[0])
    y = max(ai[1], bi[1])
    w = min(ai[2], bi[2]) - x
    h = min(ai[3], bi[3]) - y
    if w < 0 or h < 0:
        return 0
    return w*h

#交并比
def iou(a, b):
    # a and b should be (x1,y1,x2,y2)

    if a[0] >= a[2] or a[1] >= a[3] or b[0] >= b[2] or b[1] >= b[3]:
        return 0.0

    area_i = intersection(a, b)
    area_u = union(a, b, area_i)

    return float(area_i) / float(area_u + 1e-6)

#图像resize
def get_new_img_size(width, height, img_min_side=600):
    if width <= height:
        f = float(img_min_side) / width
        resized_height = int(f * height)
        resized_width = img_min_side
    else:
        f = float(img_min_side) / height
        resized_width = int(f * width)
        resized_height = img_min_side

    return resized_width, resized_height


class SampleSelector:
    def __init__(self, class_count):
        # ignore classes that have zero samples
        self.classes = [b for b in class_count.keys() if class_count[b] > 0]
        self.class_cycle = itertools.cycle(self.classes)
        self.curr_class = next(self.class_cycle)

    def skip_sample_for_balanced_class(self, img_data):

        class_in_img = False

        for bbox in img_data[‘bboxes‘]:

            cls_name = bbox[‘class‘]

            if cls_name == self.curr_class:
                class_in_img = True
                self.curr_class = next(self.class_cycle)
                break

        if class_in_img:
            return False
        else:
            return True


def calc_rpn(C, img_data, width, height, resized_width, resized_height, img_length_calc_function):

    downscale = float(C.rpn_stride)
    anchor_sizes = C.anchor_box_scales
    anchor_ratios = C.anchor_box_ratios
    num_anchors = len(anchor_sizes) * len(anchor_ratios)    

    # calculate the output map size based on the network architecture

    (output_width, output_height) = img_length_calc_function(resized_width, resized_height)

    n_anchratios = len(anchor_ratios)

    # initialise empty output objectives
    y_rpn_overlap = np.zeros((output_height, output_width, num_anchors))
    y_is_box_valid = np.zeros((output_height, output_width, num_anchors))
    y_rpn_regr = np.zeros((output_height, output_width, num_anchors * 4))

    num_bboxes = len(img_data[‘bboxes‘])

    num_anchors_for_bbox = np.zeros(num_bboxes).astype(int)
    best_anchor_for_bbox = -1*np.ones((num_bboxes, 4)).astype(int)
    best_iou_for_bbox = np.zeros(num_bboxes).astype(np.float32)
    best_x_for_bbox = np.zeros((num_bboxes, 4)).astype(int)
    best_dx_for_bbox = np.zeros((num_bboxes, 4)).astype(np.float32)

    # get the GT box coordinates, and resize to account for image resizing
    gta = np.zeros((num_bboxes, 4))
    for bbox_num, bbox in enumerate(img_data[‘bboxes‘]):
        # get the GT box coordinates, and resize to account for image resizing
        gta[bbox_num, 0] = bbox[‘x1‘] * (resized_width / float(width))
        gta[bbox_num, 1] = bbox[‘x2‘] * (resized_width / float(width))
        gta[bbox_num, 2] = bbox[‘y1‘] * (resized_height / float(height))
        gta[bbox_num, 3] = bbox[‘y2‘] * (resized_height / float(height))

    # rpn ground truth

    for anchor_size_idx in range(len(anchor_sizes)):
        for anchor_ratio_idx in range(n_anchratios):
            anchor_x = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][0]
            anchor_y = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][1]   

            for ix in range(output_width):                  
                # x-coordinates of the current anchor box   
                x1_anc = downscale * (ix + 0.5) - anchor_x / 2
                x2_anc = downscale * (ix + 0.5) + anchor_x / 2  

                # ignore boxes that go across image boundaries                  
                if x1_anc < 0 or x2_anc > resized_width:
                    continue

                for jy in range(output_height):

                    # y-coordinates of the current anchor box
                    y1_anc = downscale * (jy + 0.5) - anchor_y / 2
                    y2_anc = downscale * (jy + 0.5) + anchor_y / 2

                    # ignore boxes that go across image boundaries
                    if y1_anc < 0 or y2_anc > resized_height:
                        continue

                    # bbox_type indicates whether an anchor should be a target 
                    bbox_type = ‘neg‘

                    # this is the best IOU for the (x,y) coord and the current anchor
                    # note that this is different from the best IOU for a GT bbox
                    best_iou_for_loc = 0.0

                    for bbox_num in range(num_bboxes):

                        # get IOU of the current GT box and the current anchor box
                        curr_iou = iou([gta[bbox_num, 0], gta[bbox_num, 2], gta[bbox_num, 1], gta[bbox_num, 3]], [x1_anc, y1_anc, x2_anc, y2_anc])
                        # calculate the regression targets if they will be needed
                        if curr_iou > best_iou_for_bbox[bbox_num] or curr_iou > C.rpn_max_overlap:
                            cx = (gta[bbox_num, 0] + gta[bbox_num, 1]) / 2.0
                            cy = (gta[bbox_num, 2] + gta[bbox_num, 3]) / 2.0
                            cxa = (x1_anc + x2_anc)/2.0
                            cya = (y1_anc + y2_anc)/2.0

                            tx = (cx - cxa) / (x2_anc - x1_anc)
                            ty = (cy - cya) / (y2_anc - y1_anc)
                            tw = np.log((gta[bbox_num, 1] - gta[bbox_num, 0]) / (x2_anc - x1_anc))
                            th = np.log((gta[bbox_num, 3] - gta[bbox_num, 2]) / (y2_anc - y1_anc))

                        if img_data[‘bboxes‘][bbox_num][‘class‘] != ‘bg‘:

                            # all GT boxes should be mapped to an anchor box, so we keep track of which anchor box was best
                            if curr_iou > best_iou_for_bbox[bbox_num]:
                                best_anchor_for_bbox[bbox_num] = [jy, ix, anchor_ratio_idx, anchor_size_idx]
                                best_iou_for_bbox[bbox_num] = curr_iou
                                best_x_for_bbox[bbox_num,:] = [x1_anc, x2_anc, y1_anc, y2_anc]
                                best_dx_for_bbox[bbox_num,:] = [tx, ty, tw, th]

                            # we set the anchor to positive if the IOU is >0.7 (it does not matter if there was another better box, it just indicates overlap)
                            if curr_iou > C.rpn_max_overlap:
                                bbox_type = ‘pos‘
                                num_anchors_for_bbox[bbox_num] += 1
                                # we update the regression layer target if this IOU is the best for the current (x,y) and anchor position
                                if curr_iou > best_iou_for_loc:
                                    best_iou_for_loc = curr_iou
                                    best_regr = (tx, ty, tw, th)

                            # if the IOU is >0.3 and <0.7, it is ambiguous and no included in the objective
                            if C.rpn_min_overlap < curr_iou < C.rpn_max_overlap:
                                # gray zone between neg and pos
                                if bbox_type != ‘pos‘:
                                    bbox_type = ‘neutral‘

                    # turn on or off outputs depending on IOUs
                    if bbox_type == ‘neg‘:
                        y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
                        y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0
                    elif bbox_type == ‘neutral‘:
                        y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0
                        y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0
                    elif bbox_type == ‘pos‘:
                        y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
                        y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
                        start = 4 * (anchor_ratio_idx + n_anchratios * anchor_size_idx)
                        y_rpn_regr[jy, ix, start:start+4] = best_regr

    # we ensure that every bbox has at least one positive RPN region

    for idx in range(num_anchors_for_bbox.shape[0]):
        if num_anchors_for_bbox[idx] == 0:
            # no box with an IOU greater than zero ...
            if best_anchor_for_bbox[idx, 0] == -1:
                continue
            y_is_box_valid[
                best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *
                best_anchor_for_bbox[idx,3]] = 1
            y_rpn_overlap[
                best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *
                best_anchor_for_bbox[idx,3]] = 1
            start = 4 * (best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3])
            y_rpn_regr[
                best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], start:start+4] = best_dx_for_bbox[idx, :]

    y_rpn_overlap = np.transpose(y_rpn_overlap, (2, 0, 1))
    y_rpn_overlap = np.expand_dims(y_rpn_overlap, axis=0)

    y_is_box_valid = np.transpose(y_is_box_valid, (2, 0, 1))
    y_is_box_valid = np.expand_dims(y_is_box_valid, axis=0)

    y_rpn_regr = np.transpose(y_rpn_regr, (2, 0, 1))
    y_rpn_regr = np.expand_dims(y_rpn_regr, axis=0)

    pos_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 1, y_is_box_valid[0, :, :, :] == 1))
    neg_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 0, y_is_box_valid[0, :, :, :] == 1))

    num_pos = len(pos_locs[0])

    # one issue is that the RPN has many more negative than positive regions, so we turn off some of the negative
    # regions. We also limit it to 256 regions.
    num_regions = 256

    if len(pos_locs[0]) > num_regions/2:
        val_locs = random.sample(range(len(pos_locs[0])), len(pos_locs[0]) - num_regions/2)
        y_is_box_valid[0, pos_locs[0][val_locs], pos_locs[1][val_locs], pos_locs[2][val_locs]] = 0
        num_pos = num_regions/2

    if len(neg_locs[0]) + num_pos > num_regions:
        val_locs = random.sample(range(len(neg_locs[0])), len(neg_locs[0]) - num_pos)
        y_is_box_valid[0, neg_locs[0][val_locs], neg_locs[1][val_locs], neg_locs[2][val_locs]] = 0

    y_rpn_cls = np.concatenate([y_is_box_valid, y_rpn_overlap], axis=1)
    y_rpn_regr = np.concatenate([np.repeat(y_rpn_overlap, 4, axis=1), y_rpn_regr], axis=1)

    return np.copy(y_rpn_cls), np.copy(y_rpn_regr)


class threadsafe_iter:
    """Takes an iterator/generator and makes it thread-safe by
    serializing call to the `next` method of given iterator/generator.
    """
    def __init__(self, it):
        self.it = it
        self.lock = threading.Lock()

    def __iter__(self):
        return self

    def next(self):
        with self.lock:
            return next(self.it)        


def threadsafe_generator(f):
    """A decorator that takes a generator function and makes it thread-safe.
    """
    def g(*a, **kw):
        return threadsafe_iter(f(*a, **kw))
    return g

def get_anchor_gt(all_img_data, class_count, C, img_length_calc_function, backend, mode=‘train‘):

    # The following line is not useful with Python 3.5, it is kept for the legacy
    # all_img_data = sorted(all_img_data)

    sample_selector = SampleSelector(class_count)

    while True:
        if mode == ‘train‘:
            np.random.shuffle(all_img_data)

        for img_data in all_img_data:
            try:

                if C.balanced_classes and sample_selector.skip_sample_for_balanced_class(img_data):
                    continue

                # read in image, and optionally add augmentation

                if mode == ‘train‘:
                    img_data_aug, x_img = data_augment.augment(img_data, C, augment=True)
                else:
                    img_data_aug, x_img = data_augment.augment(img_data, C, augment=False)

                (width, height) = (img_data_aug[‘width‘], img_data_aug[‘height‘])
                (rows, cols, _) = x_img.shape

                assert cols == width
                assert rows == height

                # get image dimensions for resizing
                (resized_width, resized_height) = get_new_img_size(width, height, C.im_size)

                # resize the image so that smalles side is length = 600px
                x_img = cv2.resize(x_img, (resized_width, resized_height), interpolation=cv2.INTER_CUBIC)

                try:
                    y_rpn_cls, y_rpn_regr = calc_rpn(C, img_data_aug, width, height, resized_width, resized_height, img_length_calc_function)
                except:
                    continue

                # Zero-center by mean pixel, and preprocess image

                x_img = x_img[:,:, (2, 1, 0)]  # BGR -> RGB
                x_img = x_img.astype(np.float32)
                x_img[:, :, 0] -= C.img_channel_mean[0]
                x_img[:, :, 1] -= C.img_channel_mean[1]
                x_img[:, :, 2] -= C.img_channel_mean[2]
                x_img /= C.img_scaling_factor

                x_img = np.transpose(x_img, (2, 0, 1))
                x_img = np.expand_dims(x_img, axis=0)

                y_rpn_regr[:, y_rpn_regr.shape[1]//2:, :, :] *= C.std_scaling

                if backend == ‘tf‘:
                    x_img = np.transpose(x_img, (0, 2, 3, 1))
                    y_rpn_cls = np.transpose(y_rpn_cls, (0, 2, 3, 1))
                    y_rpn_regr = np.transpose(y_rpn_regr, (0, 2, 3, 1))

                yield np.copy(x_img), [np.copy(y_rpn_cls), np.copy(y_rpn_regr)], img_data_aug

            except Exception as e:
                print(e)
                continue

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关于RPN计算部分，参考keras版faster-rcnn算法详解（1.RPN计算）