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config.py

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+""" 
+    网络相关配置文件
+"""
+
+# 预训练权重
+weight_model_path = './vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
+
+# 输入图像大小，52*52*3
+input_shape = (52,52,3)
+
+# 如果不使用GPU训练，则传入列表，多个GPU则传入多个编号
+gpu = []
+# gpu = [0,1,2,3]
+
+# 训练ecoph数
+epochs = 3000
+# 批次大小
+batch_size = 128
+
+# 学习率
+lr = 1e-3
+
+# 是否开启tensorboard
+tensorboard = True
+
+# tensorboard日志目录
+tensorboard_log_dir = './logs'
+
+# 验证集比例
+valid_rate = 0.05
+
+sample_path = './sample'
+
+auto_best_checkpoint_path = './checkpoint/best.h5'
+
+auto_epoch_checkpoint_path = './checkpoint/epoch_{epoch:03d}.h5'
+
+model_save_path = './model.h5'

pre.py

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+import cv2
+import numpy as np
+from keras.models import load_model
+from keras.layers import Lambda
+from keras import backend as K
+import os
+import random
+from PIL import Image
+
+input1 = input('请输入图片名称1：')
+input2 = input('请输入图片名称2：')
+
+output = Lambda(lambda x: K.abs(x[0] - x[1]))
+weight = "./best.h5"
+# 加载模型
+# model = load_model(weight, custom_objects={'contrastive_loss': contrastive_loss, 'binary_accuracy': binary_accuracy})
+model = load_model(weight, custom_objects={'output': output})
+
+
+resize = 52
+img1 = cv2.imread(input1)
+img2 = cv2.imread(input2)
+
+img1 = cv2.resize(img1, (resize, resize)) / 255
+img2 = cv2.resize(img2, (resize, resize)) / 255
+
+img1 = np.expand_dims(img1, axis=0)
+img2 = np.expand_dims(img2, axis=0)
+
+result = model.predict([img1, img2])
+
+print(result)
+
+

predict.py

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+import cv2
+import numpy as np
+from keras.models import load_model
+from keras.layers import Lambda
+from keras import backend as K
+import os
+import random
+from PIL import Image
+# 要预测的图片
+# image_path = "./sample/1691156257961.jpg"
+image_path = os.listdir("./data")
+# 随机选取一张图片
+# inp = input('请输入图片名称：')
+image_path = "./data/" + random.choice(image_path)
+# image_path = './sample/' + inp
+# print(image_path)
+
+""" 
+    YOLOv3 分割模型
+"""
+weight = "./yolov3-tiny_17000.weights"
+cfg = "./yolov3-tiny.cfg"
+
+# 加载模型
+net = cv2.dnn.readNet(weight, cfg)
+""" 
+    孪生网络 对比模型
+"""
+resize_height, resize_width,channel = 52,52,3
+
+# 自定义的损失和精度
+output = Lambda(lambda x: K.abs(x[0] - x[1]))
+def contrastive_loss(y_true, y_pred):
+    margin = 1
+    return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
+
+def binary_accuracy(y_true, y_pred):
+    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
+weight = "./best.h5"
+# 加载模型
+# model = load_model(weight, custom_objects={'contrastive_loss': contrastive_loss, 'binary_accuracy': binary_accuracy})
+model = load_model(weight, custom_objects={'output': output})
+
+classes = ["text"]
+
+
+img = cv2.imread(image_path)
+
+cv2.namedWindow('display')
+
+
+""" 
+    YOLO 分割出内容
+"""
+height, width, channels = img.shape
+blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+net.setInput(blob)
+outs = net.forward(net.getUnconnectedOutLayersNames())
+class_ids = []
+confidences = []
+boxes = []
+
+for out in outs:
+    for detection in out:
+        scores = detection[5:]
+        class_id = np.argmax(scores)
+        confidence = scores[class_id]
+
+        if confidence > 0.1:
+            center_x = int(detection[0] * width)
+            center_y = int(detection[1] * height)
+            w = int(detection[2] * width)
+            h = int(detection[3] * height)
+
+            x = int(center_x - w / 2)
+            y = int(center_y - h / 2)
+
+            boxes.append([x, y, w, h])
+            confidences.append(float(confidence))
+            class_ids.append(class_id)
+
+indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.1)
+
+thickness = 2
+color = (0, 255, 0)
+font = cv2.FONT_HERSHEY_PLAIN
+
+new_boxes = []
+
+for i in range(len(boxes)):
+    if i in indexes:
+        x, y, w, h = boxes[i]
+        
+        new_boxes.append([x, y, w, h])
+
+total = len(new_boxes)
+
+print('>>> 检测出：', total,'个字符')
+
+top_total = total / 2
+# 如果取出来有小数
+if top_total % 1 != 0:
+    print('>>> YOLO分割有误，顶部字符数量与点选数量不匹配')
+    exit()
+    
+top_total = int(top_total)
+
+# 取出w最大的（需要点选的）
+w_max_boxes = sorted(new_boxes, key=lambda x: x[2], reverse=True)[:top_total]
+
+# 取出剩下的（需要对比的）
+w_min_box = sorted(new_boxes, key=lambda x: x[2], reverse=True)[top_total:]
+
+# 按照从左到右排序w_min_box
+w_min_box = sorted(w_min_box, key=lambda x: x[0])
+
+
+w_max_image = []
+w_min_image = []
+
+# 分割出具体图像
+for i in range(top_total):
+    x, y, w, h = w_max_boxes[i]
+    p = cv2.resize(img[y:y+h, x:x+w], (resize_height, resize_width))
+    
+    # cv2.imwrite('./1/1_{}.jpg'.format(i), p)
+    
+    w_max_image.append(p)
+    
+for i in range(len(w_min_box)):
+    x, y, w, h = w_min_box[i]
+    p = cv2.resize(img[y:y+h, x:x+w], (resize_height, resize_width))
+    
+    # cv2.imwrite('./1/2_{}.jpg'.format(i), p)
+    
+    w_min_image.append(p)
+    
+# print(w_max_boxes)
+
+w_max_image_np = np.array(w_max_image) / 255
+w_min_image_np = np.array(w_min_image) / 255
+
+select_index = []
+
+# 开始挨个对比，取出最相似的
+for i in range(len(w_max_image_np)):
+    print('>>> 开始对比第', i+1, '个字符')
+    cv2.imwrite('./1/1_{}.jpg'.format(i), w_max_image_np[i] * 255)
+    
+    left_x = w_max_image_np[i]
+    num_index = 0
+    cache_rate = 0
+    for k in range(len(w_min_image_np)):
+        left_y = w_min_image_np[k]
+        predict = model.predict([left_x.reshape(1, resize_height, resize_width, channel), left_y.reshape(1, resize_height, resize_width, channel)])
+        rate = predict[0][0]
+        if rate > cache_rate:
+            cv2.imwrite('./1/2_{}.jpg'.format(i), w_min_image_np[k] * 255)
+            num_index = k
+            
+            
+        
+        cache_rate = rate
+        
+
+    select_index.append(num_index)
+    
+print('>>> 对比完成，结果为：', select_index)
+
+location = []
+
+for i in range(len(select_index)):
+    x, y, w, h = w_max_boxes[select_index[i]]
+    cv2.rectangle(img, (x, y), (x+w, y+h), color, thickness)
+    cv2.putText(img, str(i+1), (x, y+h), font, 1, color, thickness)
+    # 转换为图片坐标中心点
+    location.append([x+w/2, y+h/2])
+    
+print('>>> 位置坐标：', location)
+    
+cv2.imshow('display', img)
+cv2.waitKey(0)

siamese.py

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+from tensorflow.keras.layers import Input,Lambda,Dense
+from tensorflow.keras.models import Model
+from vgg16 import VGG16
+
+def siamese(input_shape):
+    # 基础VGG，因为两个网络共享参数，所以需要是同一个实例
+    base_network = VGG16()
+    
+    left_input = Input(shape = input_shape)
+    right_input = Input(shape = input_shape)
+    
+    left_output = base_network.call(left_input)
+    right_output = base_network.call(right_input)
+    
+    # l1距离
+    output = Lambda(lambda x:abs(x[0]-x[1]))([left_output,right_output])
+    
+    # 全连接层1
+    output = Dense(512,activation = 'relu')(output)
+    
+    # 全连接层2
+    output = Dense(1,activation = 'sigmoid')(output)
+    
+    return Model([left_input,right_input],output)

train.py

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+
+import os
+import config
+import tensorflow as tf
+from siamese import siamese
+from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.callbacks import *
+import cv2
+import random
+import numpy as np
+import utils
+
+
+if len(config.gpu) >= 1:
+    # 设置显存分配方式
+    gpus = tf.config.experimental.list_physical_devices('GPU')
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+        
+    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(i) for i in config.gpu)
+    print('>>> GPU:',os.environ['CUDA_VISIBLE_DEVICES'])
+input_shape = config.input_shape
+
+model = siamese(input_shape=(input_shape[0],input_shape[1],input_shape[2]))
+
+if config.weight_model_path != False:
+    """ 
+        by_name=True：设置为 True 时，表示根据层的名称进行权重的匹配和加载。这意味着只有具有相同名称的层才会被加载权重，其他层将被忽略。这在微调模型时非常有用，可以只加载与预训练权重文件中的层名称匹配的部分权重。
+
+        skip_mismatch=True：设置为 True 时，表示如果层数量不匹配或找不到对应的层，则跳过权重加载的错误。这在模型的结构发生变化时很有用，可以避免由于层数不匹配而导致的加载错误。请注意，在层数量不匹配的情况下，任何未匹配的层都不会加载权重。
+    """
+    model.load_weights(config.weight_model_path,by_name=True,skip_mismatch=True)
+    
+# 如果是多个GPU需要设置
+if len(config.gpu) > 1:
+    model = multi_gpu_model(model,gpus=len(config.gpu))
+
+opt = SGD(lr=config.lr,momentum=0.9)
+
+min_lr = config.lr * 0.01
+
+nbs             = 64
+lr_limit_max    = 1e-3
+lr_limit_min    = 3e-4
+Init_lr_fit     = min(max(config.batch_size / nbs * config.lr, lr_limit_min), lr_limit_max)
+Min_lr_fit      = min(max(config.batch_size / nbs * min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
+
+# 准备数据
+sample = os.listdir(config.sample_path)
+data = []
+hasOne = []
+for i in sample:
+    t = i.split('_')[0]
+    if t not in hasOne:
+        hasOne.append(t)
+        data.append([1, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
+        # 需要做数据增强的部分，到时候会把2转换成1
+        data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
+        data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
+        data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
+        
+        # 随机负样本
+        for j in range(3):
+            f = random.choice(sample)
+            while f.split('_')[0] == t:
+                f = random.choice(sample)
+                
+            data.append([0, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + f])
+
+# 打乱数据
+random.shuffle(data)
+
+train_x_left = np.zeros((len(data),input_shape[0],input_shape[1],input_shape[2]))
+train_x_right = np.zeros((len(data),input_shape[0],input_shape[1],input_shape[2]))
+
+train_y = np.zeros((len(data),1))
+
+total = len(data)
+now = 0
+for i in data:
+    now += 1 
+    left = cv2.imread(i[1])
+    left = cv2.resize(left,(input_shape[0],input_shape[1]))
+    
+    right = cv2.imread(i[2])
+    right = cv2.resize(right,(input_shape[0],input_shape[1]))
+    
+    label = i[0]
+    
+    # 数据增强操作
+    if label == 2:
+        label = 1
+        # 随机翻转
+        if random.random() > 0.5:
+            left = cv2.flip(left,1)
+            right = cv2.flip(right,1)
+        
+        # 随机噪声
+        for h in range(5):
+            # 加上随机线条
+            if random.random() > 0.5:
+                cv2.line(left,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(0,0,0),random.randint(1,3))
+                cv2.line(right,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(0,0,0),random.randint(1,3))
+                
+            # 加上随机点
+            if random.random() > 0.5:
+                cv2.circle(left,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),random.randint(1,3),(0,0,0),-1)
+                cv2.circle(right,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),random.randint(1,3),(0,0,0),-1)
+            
+        # 色域变换
+        if random.random() > 0.5:
+            left = cv2.cvtColor(left,cv2.COLOR_BGR2HSV)
+            right = cv2.cvtColor(right,cv2.COLOR_BGR2HSV)
+                
+            left[:,:,0] = left[:,:,0] + random.randint(-10,10)
+            right[:,:,0] = right[:,:,0] + random.randint(-10,10)
+                
+            left = cv2.cvtColor(left,cv2.COLOR_HSV2BGR)
+            right = cv2.cvtColor(right,cv2.COLOR_HSV2BGR)
+            
+        if not os.path.exists('./1.jpg'):
+            cv2.imwrite('./1.jpg',left)
+            cv2.imwrite('./2.jpg',right)
+    
+    
+    # 归一化
+    left = left / 255.0
+    right = right / 255.0
+     
+    train_x_left[now-1] = left
+    train_x_right[now-1] = right
+    train_y[now-1] = label
+    
+    
+    
+    print('>>> 正在处理第',now,'/',total,'张图片',end='\r')
+    
+
+lr_scheduler_func = utils.get_lr_scheduler('cos', Init_lr_fit, Min_lr_fit, config.epochs)
+
+if config.tensorboard:
+    tb = TensorBoard(log_dir=config.tensorboard_log_dir,write_graph=True,write_images=True)
+    
+best_checkpoint = ModelCheckpoint(config.auto_best_checkpoint_path,save_weights_only=False,save_best_only=True,verbose=1,period=1)
+epoch_checkpoint = ModelCheckpoint(config.auto_epoch_checkpoint_path,save_weights_only=False,save_best_only=False,verbose=1,period=10)
+lr_scheduler = LearningRateScheduler(lr_scheduler_func, verbose=1)
+
+callback = [best_checkpoint,epoch_checkpoint,lr_scheduler]
+
+if config.tensorboard:
+    callback.append(tb)
+    
+
+model.compile(loss='binary_crossentropy',optimizer=opt,metrics=['binary_accuracy'])
+
+
+model.fit([train_x_left,train_x_right],train_y,batch_size=config.batch_size,epochs=config.epochs,validation_split=config.valid_rate,callbacks=callback)
+model.save_weights(config.model_save_path)

utils.py

@@ -0,0 +1,41 @@
+import math
+from functools import partial
+
+def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):
+    def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):
+        if iters <= warmup_total_iters:
+            # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
+            lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2
+            ) + warmup_lr_start
+        elif iters >= total_iters - no_aug_iter:
+            lr = min_lr
+        else:
+            lr = min_lr + 0.5 * (lr - min_lr) * (
+                1.0
+                + math.cos(
+                    math.pi
+                    * (iters - warmup_total_iters)
+                    / (total_iters - warmup_total_iters - no_aug_iter)
+                )
+            )
+        return lr
+
+    def step_lr(lr, decay_rate, step_size, iters):
+        if step_size < 1:
+            raise ValueError("step_size must above 1.")
+        n       = iters // step_size
+        out_lr  = lr * decay_rate ** n
+        return out_lr
+
+    if lr_decay_type == "cos":
+        warmup_total_iters  = min(max(warmup_iters_ratio * total_iters, 1), 3)
+        warmup_lr_start     = max(warmup_lr_ratio * lr, 1e-6)
+        no_aug_iter         = min(max(no_aug_iter_ratio * total_iters, 1), 15)
+        func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)
+    else:
+        decay_rate  = (min_lr / lr) ** (1 / (step_num - 1))
+        step_size   = total_iters / step_num
+        func = partial(step_lr, lr, decay_rate, step_size)
+
+    return func
+

vgg16.py

@@ -0,0 +1,72 @@
+from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D
+""" 
+    VGG16代码来源于https://github.com/bubbliiiing/Siamese-keras
+    
+    也可以使用tensorflow.keras.applications.VGG16实现
+"""
+
+class VGG16:
+    def __init__(self):
+        # 第一个卷积部分
+        # 105, 105, 3 -> 105, 105, 64 -> 52, 52, 64
+        self.block1_conv1 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',name = 'block1_conv1')
+        self.block1_conv2 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',name = 'block1_conv2')
+        self.block1_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block1_pool')
+        
+        # 第二个卷积部分
+        # 52, 52, 64 -> 52, 52, 128 -> 26, 26, 128
+        self.block2_conv1 = Conv2D(128, (3,3), activation = 'relu', padding = 'same',name = 'block2_conv1')
+        self.block2_conv2 = Conv2D(128, (3,3), activation = 'relu', padding = 'same',name = 'block2_conv2')
+        self.block2_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block2_pool')
+
+        # 第三个卷积部分
+        # 26, 26, 128-> 26, 26, 256 -> 13, 13, 256
+        self.block3_conv1 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv1')
+        self.block3_conv2 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv2')
+        self.block3_conv3 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv3')
+        self.block3_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block3_pool')
+
+        # 第四个卷积部分
+        # 13, 13, 256-> 13, 13, 512 -> 6, 6, 512
+        self.block4_conv1 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv1')
+        self.block4_conv2 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv2')
+        self.block4_conv3 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv3')
+        self.block4_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block4_pool')
+
+        # 第五个卷积部分
+        # 6, 6, 512-> 6, 6, 512 -> 3, 3, 512
+        self.block5_conv1 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv1')
+        self.block5_conv2 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv2')
+        self.block5_conv3 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv3')   
+        self.block5_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block5_pool')
+
+        # 3*3*512 = 4500 + 90 + 18 = 4608
+        self.flatten = Flatten(name = 'flatten')
+
+    def call(self, inputs):
+        x = inputs
+        x = self.block1_conv1(x)
+        x = self.block1_conv2(x)
+        x = self.block1_pool(x)
+
+        x = self.block2_conv1(x)
+        x = self.block2_conv2(x)
+        x = self.block2_pool(x)
+
+        x = self.block3_conv1(x)
+        x = self.block3_conv2(x)
+        x = self.block3_conv3(x)
+        x = self.block3_pool(x)
+        
+        x = self.block4_conv1(x)
+        x = self.block4_conv2(x)
+        x = self.block4_conv3(x)
+        x = self.block4_pool(x)
+
+        x = self.block5_conv1(x)
+        x = self.block5_conv2(x)
+        x = self.block5_conv3(x)
+        x = self.block5_pool(x)
+
+        outputs = self.flatten(x)
+        return outputs

yolov3-tiny.cfg

@@ -0,0 +1,182 @@
+[net]
+# Testing
+# batch=1
+# subdivisions=1
+# Training
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 30000
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=1
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 8
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask = 0,1,2
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=1
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1