参考:

Cascade Classifier Training
[OpenCV3]级联分类器训练——traincascade快速使用详解

级联分类器介绍

• 文章总概：Cascade分类器全程级联增强弱分类器(boosted cascade of weak classifiers)，而使用此分类器主要包括两方面------训练&定位。而麻烦的在于训练，如何训练，正是这篇文章将要介绍的：1.收集数据，2.准备训练数据以及训练模型。
• 个人理解：人脸检测中一个比较好的算法是mtcnn网络，也是一个级联分类器，与Cascade的区别在于一个用的卷积神经网络，一个用的是Haar或者LBP提取特征。而弱分类器的原理可以总结为：一个分类器通过学习而能够分辨的特征的能力有限，但如果让多个分类器去学习识别不同的特征，这样通过多个分类器进行分类后，所产生的结果错误率将极大降低。

数据准备

• 可以通过官方给的样本做正样本标签，也可以通过此工具labelImg，建议使用后者，后者在大数据集方面使用较多(列如微软VOC数据集)。

正样本以及负样本生成

正负样本生成脚本

import sys
import numpy as np
import  .etree.ElementTree as ET
import cv2
import os
import numpy.random as npr
from utils import IoU
from utils import ensure_directory_exists

save_dir = \"/home/rui\"
anno_path = \"./firepos/annotation\"
im_dir = \"./firepos/images\"
pos_save_dir = os.path.join(save_dir, \"./res/positive\")
neg_save_dir = os.path.join(save_dir, \'./res/negative\')

ensure_directory_exists(pos_save_dir)
ensure_directory_exists(neg_save_dir)


names_  = os.listdir(anno_path)

img_rule_h = 45
img_rule_w = 45

size = img_rule_h

num = len(names_ )
print \"%d pics in total\" % num
p_idx = 0 # positive
n_idx = 0 # negative
d_idx = 0 # dont care
idx = 0
box_idx = 0
for ne_  in names_ :
    tree = ET.parse(os.path.join(anno_path, ne_ ))
    root = tree.getroot()
    loc_bbox = []
    width_  = root.find(\"size\").find(\"width\").text
    height_  = root.find(\"size\").find(\"height\").text
    for node in root.findall(\' \'):
        label_ = node.find(\'name\').text
        if label_ == \"fire\":
            xmin_ = node.find(\'bndbox\').find(\'xmin\').text
            ymin_ = node.find(\'bndbox\').find(\'ymin\').text
            xmax_ = node.find(\'bndbox\').find(\'xmax\').text
            ymax_ = node.find(\'bndbox\').find(\'ymax\').text
            loc_bbox.append(xmin_)
            loc_bbox.append(ymin_)
            loc_bbox.append(xmax_)
            loc_bbox.append(ymax_)
    im_path = \"{}/{}\".format(im_dir, ne_ .split(\".\")[0])
    if os.path.exists(im_path + \".jpg\"):
        im_path = \"{}.jpg\".format(im_path)
    else:
        im_path = \"{}.JPG\".format(im_path)
    boxes = np.array(loc_bbox, dtype=np.float32).reshape(-1, 4)
    img = cv2.imread(im_path)
    h, w, c =img.shape
    if h != int(height_ ) or w != int(width_ ):
        print h, height_ ,w,width_ 
        continue
    idx += 1
    if idx % 100 == 0:
        print idx, \"images done\"

    height, width, channel = img.shape

    neg_num = 0
    while neg_num < 700:
        size_new = 0.0
        if width > height:
            size_new = npr.randint(img_rule_h + 1, max(img_rule_h, height / 2 - 1))
        else:
            size_new = npr.randint(img_rule_w + 1, max(img_rule_w, width / 2 - 1))

        size_new = int(size_new)
        nx = npr.randint(0, width - size_new)
        ny = npr.randint(0, height - size_new)
        crop_box = np.array([nx, ny, nx + size_new, ny + size_new])
        Iou = IoU(crop_box, boxes)
        cropped_im = img[ny : ny + size_new, nx : nx + size_new, :]
        resized_im = cv2.resize(cropped_im, (img_rule_w, img_rule_h), interpolation=cv2.INTER_LINEAR)

        if len(Iou) != 0:
            if np.max(Iou) < 0.1:
            # Iou with all gts must below 0.3
                save_file = os.path.join(neg_save_dir, \"%s.jpg\"%n_idx)
                cv2.imwrite(save_file, resized_im)
                n_idx += 1
                neg_num += 1
        else:
            # Iou with all gts must below 0.3

            save_file = os.path.join(neg_save_dir, \"%s.jpg\"%n_idx)
            cv2.imwrite(save_file, resized_im)
            n_idx += 1
            neg_num += 1

    for box in boxes:
        # box (x_left, y_top, x_right, y_bottom)
        x1, y1, x2, y2 = box
        w = x2 - x1 + 1
        h = y2 - y1 + 1

#        if float(w) / h < 2:
#            continue

        # ignore small faces
        # in case the ground truth boxes of small faces are not accurate
        if w < img_rule_w or h < img_rule_h or x1 < 0 or y1 < 0:
            continue

        # generate positive examples and part faces
        pos_nums = 300
        while pos_nums > 0:
            size_new = npr.randint(int(pow(w * h, 0.5) - 1), int(max(w, h)))
            # delta here is the offset of box center

            delta_x = npr.randint(int(-size_new * 0.1), int(size_new * 0.1))
            delta_y = npr.randint(int(-size_new * 0.1), int(size_new * 0.1))

            nx1 = max(x1 + w / 2 + delta_x - size_new / 2, 0)
            ny1 = max(y1 + h / 2 + delta_y - size_new / 2, 0)
            nx2 = min(width, nx1 + size_new)
            ny2 = min(height, ny1 + size_new)
            if nx2 > width or ny2 > height:
                continue
            crop_box = np.array([nx1, ny1, nx2, ny2])

            cropped_im = img[int(ny1) : int(ny2), int(nx1) : int(nx2), :]
            resized_im = cv2.resize(cropped_im, (img_rule_w, img_rule_h))

            box_ = box.reshape(1, -1)

            pos_nums -= 1
            save_file = os.path.join(pos_save_dir, \"%s.jpg\"%p_idx)
            cv2.imwrite(save_file, resized_im)
            p_idx += 1
        box_idx += 1
        print \"%s images done, pos: %s, neg: %s\"%(idx, p_idx, n_idx)

将正样本写入

import os

pos_dir = \"/home/rui/res/positive\"

pos_list = os.listdir(pos_dir)

f = open(\"/home/rui/temp.txt\", \"w\")

for im in pos_list:
    name = \"positive/{} 1 0 0 45 45\\n\".format(im)
    print name
    f.writelines(name)
f.close()

find -name *.jpg >> neg.txt

待更新