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)