基于TensorFlow+OpenCV的焊缝识别

基于TensorFlow+OpenCV的焊缝识别

原GitHub项目 跑通后进行了优化和进度显示

资源：
https://download.csdn.net/download/weixin_53403301/85244581

训练部分代码：

import numpy as np
import cv2
import os
import random
import tensorflow as tf

h,w = 512,512

def create_model():

    inputs = tf.keras.layers.Input(shape=(h,w,3))

    conv1 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',padding='same')(inputs)
    pool1 = tf.keras.layers.MaxPool2D()(conv1)

    conv2 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',padding='same')(pool1)
    pool2 = tf.keras.layers.MaxPool2D()(conv2)

    conv3 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')(pool2)
    pool3 = tf.keras.layers.MaxPool2D()(conv3)

    conv4 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')(pool3)

    upsm5 = tf.keras.layers.UpSampling2D()(conv4)
    upad5 = tf.keras.layers.Add()([conv3,upsm5])
    conv5 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',padding='same')(upad5)

    upsm6 = tf.keras.layers.UpSampling2D()(conv5)
    upad6 = tf.keras.layers.Add()([conv2,upsm6])
    conv6 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',padding='same')(upad6)

    upsm7 = tf.keras.layers.UpSampling2D()(conv6)
    upad7 = tf.keras.layers.Add()([conv1,upsm7])
    conv7 = tf.keras.layers.Conv2D(1,(3,3),activation='relu',padding='same')(upad7)

    model = tf.keras.models.Model(inputs=inputs, outputs=conv7)

    return model

images = []
labels = []

files = os.listdir('./dataset/images/')
random.shuffle(files)

for f in files:
    img = cv2.imread('./dataset/images/' + f)
    parts = f.split('_')
    label_name = './dataset/labels/' + 'W0002_' + parts[1]
    label = cv2.imread(label_name,2)

    img = cv2.resize(img,(w,h))
    label = cv2.resize(label,(w,h))

    images.append(img)
    labels.append(label)

images = np.array(images)
labels = np.array(labels)
labels = np.reshape(labels,
    (labels.shape[0],labels.shape[1],labels.shape[2],1))

print(images.shape)
print(labels.shape)

images = images/255
labels = labels/255

model = tf.keras.models.load_model('my_model')

model = create_model()  # uncomment this to create a new model
print(model.summary())

model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
model.fit(images,labels,epochs=100,batch_size=10)
model.evaluate(images,labels)

model.save('my_model')

识别及标定代码：

import numpy as np
import cv2
#import matplotlib.pyplot as plt
#plt.rcParams['font.sans-serif'] = ['SimHei'] # 载入字体
import os
import random
import tensorflow as tf

h,w = 512,512
num_cases = 3

images = []
labels = []

files = os.listdir('./dataset/images/')
random.shuffle(files)

model = tf.keras.models.load_model('my_model')

lowSevere = 1
midSevere = 2
highSevere = 4

#def pshow(words,picture):
#    plt.imshow(picture[:,:,::-1])
#    plt.title(words), plt.xticks([]), plt.yticks([])
#    plt.show()
    
for f in files[0:num_cases]:
    print(0,f)
    test_img = cv2.imread('./dataset/images/' + f)
    resized_img = cv2.resize(test_img,(w,h))
    resized_img = resized_img/255
    cropped_img = np.reshape(resized_img,
          (1,resized_img.shape[0],resized_img.shape[1],resized_img.shape[2]))
    
    print(1,f)
    
    test_out = model.predict(cropped_img)

    test_out = test_out[0,:,:,0]*1000
    test_out = np.clip(test_out,0,255)

    resized_test_out = cv2.resize(test_out,(test_img.shape[1],test_img.shape[0]))
    resized_test_out = resized_test_out.astype(np.uint16)

    test_img = test_img.astype(np.uint16)

    grey = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
    
    print("LOOP:",f)
    img_len = test_img.shape[0]
    print(img_len)
    for i in range(img_len):
        print("LOOP:",f,str(int(i/img_len*100))+"%")
        for j in range(test_img.shape[1]):
            if(grey[i,j]>150 & resized_test_out[i,j]>40):
                test_img[i,j,1]=test_img[i,j,1] + resized_test_out[i,j]
                resized_test_out[i,j] = lowSevere
            elif(grey[i,j]<100 & resized_test_out[i,j]>40):
                test_img[i,j,2]=test_img[i,j,2] + resized_test_out[i,j]
                resized_test_out[i,j] = highSevere
            elif(resized_test_out[i,j]>40):
                test_img[i,j,0]=test_img[i,j,0] + resized_test_out[i,j]
                resized_test_out[i,j] = midSevere
            else:
                resized_test_out[i,j] = 0

    print("END_LOOP:",f)
    M = cv2.moments(resized_test_out)
    maxMomentArea = resized_test_out.shape[1]*resized_test_out.shape[0]*highSevere
    print("0th Moment = " , (M["m00"]*100/maxMomentArea), "%")

    test_img = np.clip(test_img,0,255)

    test_img = test_img.astype(np.uint8)
    
#    pshow(str(f),test_img)
#    cv2.imshow(str(f),test_img)
    cv2.imwrite('./save/end_'+f,test_img)
#    cv2.waitKey(0)

训练进度显示：

检测图：

识别结果：