MTCNN-Caffe（一）随机Crop图片分成3类
在做MTCNN时，首先训练12Net，目的是为了提高Face的召回率，即首先进行三分类：pos，neg，part
genetate size为12的py文件：
import sys
#sys.path.append('../12net')
import numpy as np
import cv2
import os
import numpy.random as npr
sys.path.append('C:/Users/20181126/Desktop')
from IOU import IoU,IOU
#from utils import IoU
path='C:/Users/20181126/Desktop'
anno_file = os.path.join(path,"annotation_nodetect.txt")
im_dir = path
save_dir = os.path.join(path,"save")
pos_save_dir = save_dir+"/positive"
part_save_dir = save_dir+"/part"
neg_save_dir = save_dir+'/negative'
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(neg_save_dir):
os.mkdir(neg_save_dir)
if not os.path.exists(part_save_dir):
os.mkdir(part_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)
p_idx = 0 # positive
n_idx = 0 # negative
d_idx = 0 # dont care
idx = 0
box_idx = 0
for annotation in annotations:
annotation = annotation.strip().split(' ')
#image path
im_path = annotation[0]
#boxed change to float type
bbox = map(float, annotation[1:])
bbox=list(bbox)
#gt
boxes = np.array(bbox, dtype=np.float32).reshape(-1, 4)
#print(boxes)
#load image
print(os.path.join(im_dir, im_path ))
img = cv2.imread(os.path.join(im_dir, im_path))
idx += 1
if idx % 100 == 0:
print (idx, "images done")
height, width, channel = img.shape
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
#print(boxes)
Iou = IoU(crop_box, boxes)
print(Iou)
cropped_im = img[ny : ny + size, nx : nx + size, :]
resized_im = cropped_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.png"%n_idx)
f2.write(save_dir+"/negative/%s.png"%n_idx + ' 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n')
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
#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(10):
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 = cropped_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.png" % n_idx)
f2.write(save_dir+"/negative/%s.png"%n_idx + ' 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\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 = cropped_im#cv2.resize(cropped_im, (12, 12), interpolation=cv2.INTER_LINEAR)
box_ = box.reshape(1, -1)
'''print("box is")
print(box)
print("box_ is")
print(box_)
exit()'''
if IoU(crop_box, box_)[0] >= 0.65:
save_file = os.path.join(pos_save_dir, "%s.png"%p_idx)
f1.write(save_dir+"/positive/%s.png"%p_idx + ' 1 %.2f %.2f %.2f %.2f -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n'%(offset_x1, offset_y1, offset_x2, offset_y2))
cv2.imwrite(save_file, resized_im)
p_idx += 1
elif IoU(crop_box, box_)[0] >= 0.4:
save_file = os.path.join(part_save_dir, "%s.png"%d_idx)
f3.write(save_dir+"/part/%s.png"%d_idx + ' -1 %.2f %.2f %.2f %.2f -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\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))
print ("total %d boxes"%box_idx)
f1.close()
f2.close()
f3.close()