为了方便下一步的算法设计，本文对Github上的MTCNN代码进行一个粗略的分析。
项目地址：https://github.com/AITTSMD/MTCNN-Tensorflow

代码架构

该项目是基于Tensorflow架构，使用python语言写成。所需要的环境如下：

• Tensorflow 1.2.1
• TF-Slim
• Python 2.7
• Ubuntu 16.04
• Cuda 8.0

目录结构：

MTCNN-TF/
├── data
│ └── MTCNN_model
│ ├── ONet_landmark
│ ├── PNet_landmark
│ └── RNet_landmark
├── Detection
├── logs
│ ├── ONet
│ ├── PNet
│ └── RNet
├── prepare_data
├── result
├── test
└── train_models

其中，作者将与数据预处理相关的代码放在了prepare_data文件夹下。这部分写的非常的冗余，是后面代码进行优化的重要部分。

与训练相关的代码，放在了train_models里面。值得注意的是，3个模型：PNet, RNet, ONet是分别进行训练的。

训练PNet

与PNet相关的代码分别为：

• /prepare_data/gen_12net_data.py
• /prepare_data/gen_landmark_aug_12.py
• /prepare_data/gen_imglist_pnet.py
• /prepare_data/gen_PNet_tfrecords.py
• /train_models/train_PNet.py
• /train_models/train.py

下面分别进行分析。

gen_12net_data.py

准备阶段：

• 建立文件夹
• 读取annotation文件

anno_file = "wider_face_train.txt"
im_dir = "WIDER_train/images"
pos_save_dir = "12/positive"
part_save_dir = "12/part"
neg_save_dir = '12/negative'
save_dir = "./12"
if not os.path.exists(save_dir):
    os.mkdir(save_dir)
if not os.path.exists(pos_save_dir):
    os.mkdir(pos_save_dir)
if not os.path.exists(part_save_dir):
    os.mkdir(part_save_dir)
if not os.path.exists(neg_save_dir):
    os.mkdir(neg_save_dir)

f1 = open(os.path.join(save_dir, 'pos_12.txt'), 'w')
f2 = open(os.path.join(save_dir, 'neg_12.txt'), 'w')
f3 = open(os.path.join(save_dir, 'part_12.txt'), 'w')
with open(anno_file, 'r') as f:
    annotations = f.readlines()
num = len(annotations)
print "%d pics in total" % num

处理阶段（一个大循环）：

处理：

annotation = annotation.strip().split(' ')
#image path
im_path = annotation[0]
#boxed change to float type
bbox = map(float, annotation[1:])
#gt
boxes = np.array(bbox, dtype=np.float32).reshape(-1, 4)
#load image
img = cv2.imread(os.path.join(im_dir, im_path + '.jpg'))
idx += 1
if idx % 100 == 0:
    print idx, "images done"
    
height, width, channel = img.shape

处理负样本：

neg_num = 0
#1---->50
while neg_num < 50:
    #neg_num's size [40,min(width, height) / 2],min_size:40 
    size = npr.randint(12, min(width, height) / 2)
    #top_left
    nx = npr.randint(0, width - size)
    ny = npr.randint(0, height - size)
    #random crop
    crop_box = np.array([nx, ny, nx + size, ny + size])
    #cal iou
    Iou = IoU(crop_box, boxes)
    
    cropped_im = img[ny : ny + size, nx : nx + size, :]
    resized_im = cv2.resize(cropped_im, (12, 12), interpolation=cv2.INTER_LINEAR)

    if np.max(Iou) < 0.3:
        # Iou with all gts must below 0.3
        save_file = os.path.join(neg_save_dir, "%s.jpg"%n_idx)
        f2.write("12/negative/%s.jpg"%n_idx + ' 0\n')
        cv2.imwrite(save_file, resized_im)
        n_idx += 1
        neg_num += 1

处理正样本：

#as for 正 part样本
for box in boxes:
    # box (x_left, y_top, x_right, y_bottom)
    x1, y1, x2, y2 = box
    #gt's width
    w = x2 - x1 + 1
    #gt's height
    h = y2 - y1 + 1

    # ignore small faces
    # in case the ground truth boxes of small faces are not accurate
    if max(w, h) < 40 or x1 < 0 or y1 < 0:
        continue
    for i in range(5):
        size = npr.randint(12, min(width, height) / 2)
        # delta_x and delta_y are offsets of (x1, y1)
        delta_x = npr.randint(max(-size, -x1), w)
        delta_y = npr.randint(max(-size, -y1), h)
        nx1 = int(max(0, x1 + delta_x))
        ny1 = int(max(0, y1 + delta_y))
        if nx1 + size > width or ny1 + size > height:
            continue
        crop_box = np.array([nx1, ny1, nx1 + size, ny1 + size])
        Iou = IoU(crop_box, boxes)

        cropped_im = img[ny1: ny1 + size, nx1: nx1 + size, :]
        resized_im = cv2.resize(cropped_im, (12, 12), interpolation=cv2.INTER_LINEAR)

        if np.max(Iou) < 0.3:
            # Iou with all gts must below 0.3
            save_file = os.path.join(neg_save_dir, "%s.jpg" % n_idx)
            f2.write("12/negative/%s.jpg" % n_idx + ' 0\n')
            cv2.imwrite(save_file, resized_im)
            n_idx += 1
    # generate positive examples and part faces
    for i in range(20):
        # pos and part face size [minsize*0.8,maxsize*1.25]
        size = npr.randint(int(min(w, h) * 0.8), np.ceil(1.25 * max(w, h)))

        # delta here is the offset of box center
        delta_x = npr.randint(-w * 0.2, w * 0.2)
        delta_y = npr.randint(-h * 0.2, h * 0.2)
        #show this way: nx1 = max(x1+w/2-size/2+delta_x)
        nx1 = int(max(x1 + w / 2 + delta_x - size / 2, 0))
        #show this way: ny1 = max(y1+h/2-size/2+delta_y)
        ny1 = int(max(y1 + h / 2 + delta_y - size / 2, 0))
        nx2 = nx1 + size
        ny2 = ny1 + size

        if nx2 > width or ny2 > height:
            continue 
        crop_box = np.array([nx1, ny1, nx2, ny2])
        #yu gt de offset
        offset_x1 = (x1 - nx1) / float(size)
        offset_y1 = (y1 - ny1) / float(size)
        offset_x2 = (x2 - nx2) / float(size)
        offset_y2 = (y2 - ny2) / float(size)
        #crop
        cropped_im = img[ny1 : ny2, nx1 : nx2, :]
        #resize
        resized_im = cv2.resize(cropped_im, (12, 12), interpolation=cv2.INTER_LINEAR)

        box_ = box.reshape(1, -1)
        if IoU(crop_box, box_) >= 0.65:
            save_file = os.path.join(pos_save_dir, "%s.jpg"%p_idx)
            f1.write("12/positive/%s.jpg"%p_idx + ' 1 %.2f %.2f %.2f %.2f\n'%(offset_x1, offset_y1, offset_x2, offset_y2))
            cv2.imwrite(save_file, resized_im)
            p_idx += 1
        elif IoU(crop_box, box_) >= 0.4:
            save_file = os.path.join(part_save_dir, "%s.jpg"%d_idx)
            f3.write("12/part/%s.jpg"%d_idx + ' -1 %.2f %.2f %.2f %.2f\n'%(offset_x1, offset_y1, offset_x2, offset_y2))
            cv2.imwrite(save_file, resized_im)
            d_idx += 1
    box_idx += 1
print "%s images done, pos: %s part: %s neg: %s"%(idx, p_idx, d_idx, n_idx)