import sys
import numpy as np
import cv2
import os
import numpy.random as nprstdsize = 12# im_dir = "samples"
pos_save_dir = str(stdsize) + "/positive"
part_save_dir = str(stdsize) + "/part"
neg_save_dir = str(stdsize) + '/negative'
save_dir = "12"def IoU(pr_box, boxes):"""Compute IoU between detect box and gt boxesParameters:----------box: numpy array , shape (5, ): x1, y1, x2, y2, scoreinput boxboxes: numpy array, shape (n, 4): x1, y1, x2, y2input ground truth boxesReturns:-------ovr: numpy.array, shape (n, )IoU"""# print("随机锚框:",pr_box)box_area = (pr_box[2] - pr_box[0] + 1) * (pr_box[3] - pr_box[1] + 1)# print("随机面积box_area:",box_area)# print("(boxes[:, 2] - boxes[:, 0] + 1):",(boxes[:, 2] - boxes[:, 0] + 1))#XML真实区域 X2-X1 +1 = W   Y2-Y1 = H   W*Harea = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)# print("真实面积area:",area)# print("probx[0]",pr_box[0])# boxes[:, 0]代表取boxes这个nx4矩阵所有行的第一列数据xx1 = np.maximum(pr_box[0], boxes[:, 0])# print("xx1",xx1)yy1 = np.maximum(pr_box[1], boxes[:, 1])# print("yy1",yy1)xx2 = np.minimum(pr_box[2], boxes[:, 2])# print("xx2",xx2)yy2 = np.minimum(pr_box[3], boxes[:, 3])# print("yy2",yy2)# compute the width and height of the bounding box# print("xx2-xx1",(xx2-xx1))w = np.maximum(0, xx2 - xx1 + 1)h = np.maximum(0, yy2 - yy1 + 1)# inter_area = (xx1 - xx2 + 1) * (yy1 - yy2 + 1)# w = np.max(xx1,yy1)inter = w * h# print("inter",inter_area)ovr = inter / (box_area + area - inter)print("IOU:",ovr)return ovr# 生成一系列文件夹用于存储三类样本
def mkr(dr):if not os.path.exists(dr):os.mkdir(dr)mkr(save_dir)
mkr(pos_save_dir)
mkr(part_save_dir)
mkr(neg_save_dir)# 生成一系列txt文档用于存储Positive，Negative，Part三类数据的信息
f1 = open(os.path.join(save_dir, 'pos_' + str(stdsize) + '.txt'), 'w')
f2 = open(os.path.join(save_dir, 'neg_' + str(stdsize) + '.txt'), 'w')
f3 = open(os.path.join(save_dir, 'part_' + str(stdsize) + '.txt'), 'w')# 读取label.txt    pt[4,2]+class[1],四个顶点坐标，加一位分类数据
annotations= np.load("labels8.npy")#[:100,4].astype(np.float32)
imgs=np.load("imgs8.npy")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#len(annotations) 只需要100张图片
for i in range(100):boxes = annotations[i][0:8].reshape(-1, 2)ix=boxes[:,0].min()iy=boxes[:,1].min()ax=boxes[:,0].max()ay=boxes[:,1].max()boxes=np.array([[ix,iy,ax,ay]])#取最大最小img = imgs[i]# print(img.shape)idx += 1if idx % 100 == 0:print(idx, "images done")height, width, channel = img.shapeprint(img.shape)neg_num = 0while neg_num < 50:# 生成随机数，对每张数据集中的图像进行切割，生成一系列小的图像size = npr.randint(stdsize, min(width, height) / 2)nx = npr.randint(0, width - size)ny = npr.randint(0, height - size)crop_box = np.array([nx, ny, nx + size, ny + size])# print(crop_box)# print("boxes",boxes)# 计算小的图像与标注产生的检测框之间的IoUIou = IoU(crop_box, boxes)# print(Iou)cropped_im = img[ny : ny + size, nx : nx + size, :]resized_im = cv2.resize(cropped_im, (stdsize, stdsize), interpolation=cv2.INTER_LINEAR)if np.max(Iou) < 0.3:# Iou with all gts must below 0.3save_file = os.path.join(neg_save_dir, "%s.jpg"%n_idx)f2.write(str(stdsize)+"/negative/%s"%n_idx + ' 0\n')cv2.imwrite(save_file, resized_im)n_idx += 1neg_num += 1for box in boxes:print(box)# box (x_left, y_top, x_right, y_bottom)x1, y1, x2, y2 = boxw = x2 - x1 + 1h = y2 - y1 + 1# max(w, h) < 40：参数40表示忽略的最小的脸的大小# in case the ground truth boxes of small faces are not accurateif max(w, h) < 20 or x1 < 0 or y1 < 0:continue# 生成与gt有重叠的反面例子for i in range(5):size = npr.randint(stdsize, 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:continuecrop_box = np.array([nx1, ny1, nx1 + size, ny1 + size])Iou = IoU(crop_box, boxes)# cropped_im = img[ny: ny + size, nx: nx + size, :]cropped_im = img[ny1 : ny1 + size, nx1 : nx1 + size, :]resized_im = cv2.resize(cropped_im, (stdsize, stdsize), interpolation=cv2.INTER_LINEAR)if np.max(Iou) < 0.3:# Iou with all gts must below 0.3save_file = os.path.join(neg_save_dir, "%s.jpg" % n_idx)f2.write(str(stdsize)+"/negative/%s" % n_idx + ' 0\n')cv2.imwrite(save_file, resized_im)n_idx += 1# generate positive examples and part facesfor i in range(20):size = npr.randint(int(min(w, h) * 0.8), np.ceil(1.25 * max(w, h)))# delta here is the offset of box centerdelta_x = npr.randint(-w * 0.2, w * 0.2)delta_y = npr.randint(-h * 0.2, h * 0.2)nx1 = max(x1 + w / 2 + delta_x - size / 2, 0)ny1 = max(y1 + h / 2 + delta_y - size / 2, 0)nx2 = nx1 + sizeny2 = ny1 + sizeif nx2 > width or ny2 > height:continuecrop_box = np.array([nx1, ny1, nx2, ny2])offset_x1 = (x1 - nx1) / float(size)offset_y1 = (y1 - ny1) / float(size)offset_x2 = (x2 - nx2) / float(size)offset_y2 = (y2 - ny2) / float(size)cropped_im = img[int(ny1):int(ny2), int(nx1):int(nx2), :]resized_im = cv2.resize(cropped_im, (stdsize, stdsize), interpolation=cv2.INTER_LINEAR)box_ = box.reshape(1, -1)if IoU(crop_box, box_) >=0.7: #0.65:#考虑旋转save_file = os.path.join(pos_save_dir, "%s.jpg"%p_idx)f1.write(str(stdsize)+"/positive/%s"%p_idx + ' 1 %.2f %.2f %.2f %.2f\n'%(offset_x1, offset_y1, offset_x2, offset_y2))cv2.imwrite(save_file, resized_im)p_idx += 1elif IoU(crop_box, box_) >=0.5: # 0.4:#考虑旋转save_file = os.path.join(part_save_dir, "%s.jpg"%d_idx)f3.write(str(stdsize)+"/part/%s"%d_idx + ' -1 %.2f %.2f %.2f %.2f\n'%(offset_x1, offset_y1, offset_x2, offset_y2))cv2.imwrite(save_file, resized_im)d_idx += 1box_idx += 1print("%s images done, pos: %s part: %s neg: %s"%(idx, p_idx, d_idx, n_idx))f1.close()
f2.close()
f3.close()

import sys
import ossave_dir = "12"
if not os.path.exists(save_dir):os.mkdir(save_dir)
f1 = open(os.path.join(save_dir, 'pos_%s.txt'%(save_dir)), 'r')
f2 = open(os.path.join(save_dir, 'neg_%s.txt'%(save_dir)), 'r')
f3 = open(os.path.join(save_dir, 'part_%s.txt'%(save_dir)), 'r')pos = f1.readlines()
neg = f2.readlines()
part = f3.readlines()
f = open(os.path.join(save_dir, 'label-train%s.txt'%(save_dir)), 'w')for i in range(int(len(pos))):p = pos[i].find(" ") + 1pos[i] = pos[i][:p-1] + ".jpg " + pos[i][p:-1] + "\n"f.write(pos[i])for i in range(int(len(neg))):p = neg[i].find(" ") + 1neg[i] = neg[i][:p-1] + ".jpg " + neg[i][p:-1] + " -1 -1 -1 -1\n"f.write(neg[i])for i in range(int(len(part))):p = part[i].find(" ") + 1part[i] = part[i][:p-1] + ".jpg " + part[i][p:-1] + "\n"f.write(part[i])f1.close()
f2.close()
f3.close()

import os
import random
import sysimport tensorflow as tf
import cv2
from PIL import Imagedef _int64_feature(value):"""Wrapper for insert int64 feature into Example proto."""if not isinstance(value, list):value = [value]return tf.train.Feature(int64_list=tf.train.Int64List(value=value))def _float_feature(value):"""Wrapper for insert float features into Example proto."""if not isinstance(value, list):value = [value]return tf.train.Feature(float_list=tf.train.FloatList(value=value))def _bytes_feature(value):"""Wrapper for insert bytes features into Example proto."""if not isinstance(value, list):value = [value]return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))def _process_image_withoutcoder(filename):"""利用cv2将filename指向的图片tostring"""image = cv2.imread(filename)# transform data into string formatimage_data = image.tostring()assert len(image.shape) == 3height = image.shape[0]width = image.shape[1]assert image.shape[2] == 3# return string data and initial height and width of the imagereturn image_data, height, widthdef _convert_to_example_simple(image_example, image_buffer):"""covert to tfrecord fileParameter------------image_example: dict, an image exampleimage_buffer: string, JPEG encoding of RGB imageReturn-----------Example proto"""class_label = image_example['label']bbox = image_example['bbox']roi = [bbox['xmin'], bbox['ymin'], bbox['xmax'], bbox['ymax']]# landmark = [bbox['xlefteye'],bbox['ylefteye'],bbox['xrighteye'],bbox['yrighteye'],bbox['xnose'],bbox['ynose'],#             bbox['xleftmouth'],bbox['yleftmouth'],bbox['xrightmouth'],bbox['yrightmouth']]example = tf.train.Example(features=tf.train.Features(feature={'image/encoded': _bytes_feature(image_buffer),'image/label': _int64_feature(class_label),'image/roi': _float_feature(roi),# 'image/landmark': _float_feature(landmark)}))return example# 从图片和注释文件里加载数据并将其添加到TFRecord里
# 参数（变量）：filename:存有数据的字典；tfrecord_writer:用来写入TFRecord的writerdef _add_to_tfrecord(filename, image_example, tfrecord_writer):# print('---', filename)# imaga_data:转化为字符串的图片# height:图片原始高度# width:图片原始宽度# image_example：包含图片信息的字典# print(filename)image_data, height, width = _process_image_withoutcoder(filename)example = _convert_to_example_simple(image_example, image_data)tfrecord_writer.write(example.SerializeToString())  # 将imaga_data转化到image_example中并写入tfrecorddef _get_output_filename(output_dir,net):# 定义一下输出的文件名# return '%s/%s_%s_%s.tfrecord' % (output_dir, name, net, st)# st = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())# time.strftime() 函数接收以时间元组，并返回以可读字符串表示的当地时间，格式由参数format决定:time.strftime(format[, t]),用来输出当前时间# 返回的是'../../DATA/imglists/PNet/train_PNet_landmark.tfrecord'return '%s/train_%s_landmark.tfrecord' % (output_dir,net)def run(dataset_dir,net,output_dir,shuffling=False):"""运行转换操作Args:dataset_dir: 数据集所在的数据集目录output_dir: 输出目录"""# tfrecord nametf_filename = _get_output_filename(output_dir,net)  # '../../DATA/imglists/PNet/train_PNet_landmark.tfrecord'if tf.io.gfile.exists(tf_filename):  # tf.io.gfile模块提供了文件操作的API,包括文件的读取、写入、删除、复制等等print('Dataset files already exist. Exiting without re-creating them.')  # 判断是否存在同名文件return# 获得数据集，并打乱顺序dataset = get_dataset(dataset_dir)print(dataset)# filenames = dataset['filename']if shuffling:tf_filename = tf_filename + '_shuffle'# random.seed(12345454)random.shuffle(dataset)  # 打乱dataset数据集的顺序# Process dataset files.# write the data to tfrecordprint('lala')with tf.io.TFRecordWriter(tf_filename) as tfrecord_writer:for i, image_example in enumerate(dataset):  # 读取dataset的索引和内容if (i + 1) % 1 == 0:sys.stdout.write('\r>> %d/%d images has been converted' % (i + 1, len(dataset)))  # 输出“x00/ len(dataset) images has been converted”sys.stdout.flush()  # 以一定间隔时间刷新输出filename = image_example['filename']  # 赋值_add_to_tfrecord(filename, image_example, tfrecord_writer)# 最后，编写标签文件# labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))# dataset_utils.write_label_file(labels_to_class_names, dataset_dir)print('\nFinished converting the MTCNN dataset!')def get_dataset(dir):# 获取文件名字，标签和注释item = 'label-train%s.txt'%(dir)dataset_dir = os.path.join(dir, item)  # dataset_dir = '../../DATA/imglists/PNet/train_PNet_landmark.txt'# print(dataset_dir)imagelist = open(dataset_dir, 'r')  # 以只读的形式打开train_PNet_landmark.txt，并传入imagelist里面dataset = []  # 新建列表for line in imagelist.readlines():  # 按行读取imagelist里面的内容info = line.strip().split(' ')  # .strip().split()去除每一行首尾空格并且以空格为分隔符读取内容到info里面data_example = dict()  # 新建字典bbox = dict()data_example['filename'] = info[0]  # filename=info[0]# print(data_example['filename'])data_example['label'] = int(info[1])  # label=info[1]，info[1]的值有四种可能，1，0，-1，-2；分别对应着正、负、无关、关键点样本bbox['xmin'] = 0  # 初始化bounding box的值bbox['ymin'] = 0bbox['xmax'] = 0bbox['ymax'] = 0# bbox['xlefteye'] = 0  # 初始化人脸坐标的值# bbox['ylefteye'] = 0# bbox['xrighteye'] = 0# bbox['yrighteye'] = 0# bbox['xnose'] = 0# bbox['ynose'] = 0# bbox['xleftmouth'] = 0# bbox['yleftmouth'] = 0# bbox['xrightmouth'] = 0# bbox['yrightmouth'] = 0if len(info) == 6:  # 当info的长度等于6时，表示此时的info是正样本或者无关样本bbox['xmin'] = float(info[2])bbox['ymin'] = float(info[3])bbox['xmax'] = float(info[4])bbox['ymax'] = float(info[5])# if len(info) == 12:  # 当info的长度等于12时，表示此时的info是landmark样本#     bbox['xlefteye'] = float(info[2])#     bbox['ylefteye'] = float(info[3])#     bbox['xrighteye'] = float(info[4])#     bbox['yrighteye'] = float(info[5])#     bbox['xnose'] = float(info[6])#     bbox['ynose'] = float(info[7])#     bbox['xleftmouth'] = float(info[8])#     bbox['yleftmouth'] = float(info[9])#     bbox['xrightmouth'] = float(info[10])#     bbox['yrightmouth'] = float(info[11])data_example['bbox'] = bbox  # 将bounding box值传入字典dataset.append(data_example)  # 将data_example字典内容传入列表datasetreturn dataset  # 返回的是dataset，datase是个列表，但里面每个元素都是一个字典，每个字典都含有3个key，分别是filename、label和bounding boxif __name__ == '__main__':dir = '12'net = 'PNet'output_directory = '12'run(dir,net,output_directory,shuffling=True)

import tensorflow as tf
import numpy as npdef image_color_distort(inputs):inputs = tf.image.random_contrast(inputs, lower=0.5, upper=1.5)inputs = tf.image.random_brightness(inputs, max_delta=0.2)inputs = tf.image.random_hue(inputs,max_delta= 0.2)inputs = tf.image.random_saturation(inputs,lower = 0.5, upper= 1.5)return inputsdef red_tf(imgs,net_size):raw_image_dataset = tf.data.TFRecordDataset(imgs).shuffle(1000)image_feature_description = {'image/encoded': tf.io.FixedLenFeature([], tf.string),'image/label': tf.io.FixedLenFeature([], tf.int64),'image/roi': tf.io.FixedLenFeature([4], tf.float32),}def _parse_image_function(example_proto):# Parse the input tf.Example proto using the dictionary above.return tf.io.parse_single_example(example_proto, image_feature_description)parsed_image_dataset = raw_image_dataset.map(_parse_image_function)print(parsed_image_dataset)image_batch = []label_batch = []bbox_batch = []for image_features in parsed_image_dataset:image_raw = tf.io.decode_raw(image_features['image/encoded'],tf.uint8)# 将值规划在[-1,1]内images = tf.reshape(image_raw, [net_size, net_size, 3])image = (tf.cast(images, tf.float32) - 127.5) / 128#图像变色image = image_color_distort(image)image_batch.append(image)label = tf.cast(image_features['image/label'], tf.float32)label_batch.append(label)roi = tf.cast(image_features['image/roi'], tf.float32)bbox_batch.append(roi)return image_batch,label_batch,bbox_batch