深度学习水果成熟度识别系统
1 前言+ 2 开发简介+ 3 识别原理+
3.1 传统图像识别原理+ 3.2 深度学习水果识别
4 数据集+ 5 部分关键代码+
5.1 处理训练集的数据结构+ 5.2 模型网络结构+ 5.3 训练模型
6 识别效果+ 7 最后
1 前言
这两年开始，各个学校对毕设的要求越来越高，难度也越来越大… 毕业设计耗费时间，耗费精力，甚至有些题目即使是专业的老师或者硕士生也需要很长时间，所以一旦发现问题，一定要提前准备，避免到后面措手不及，草草了事。
✍作者简介：机器学习，深度学习，卷积神经网络处理，图像处理
B站项目实战：https://space.bilibili.com/364224477
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2 开发简介
深度学习作为机器学习领域内新兴并且蓬勃发展的一门学科， 它不仅改变着传统的机器学习方法， 也影响着我们对人类感知的理解， 已经在图像识别和语音识别等领域取得广泛的应用。 因此， 本文在深入研究深度学习理论的基础上， 将深度学习应用到水果图像识别中， 以此来提高了水果图像的识别性能。
3 识别原理
3.1 传统图像识别原理传统的水果图像识别系统的一般过程如下图所示，主要工作集中在图像预处理和特征提取阶段。
在大多数的识别任务中， 实验所用图像往往是在严格限定的环境中采集的， 消除了外界环境对图像的影响。 但是实际环境中图像易受到光照变化、 水果反光、 遮挡等因素的影响， 这在不同程度上影响着水果图像的识别准确率。
在传统的水果图像识别系统中， 通常是对水果的纹理、 颜色、 形状等特征进行提取和识别。
CNN 是一种专门为识别二维特征而设计的多层神经网络， 它的结构如下图所示，这种结构对平移、 缩放、 旋转等变形具有高度的不变性。
学长本次采用的 CNN 架构如图：
4 数据集
数据库分为训练集(train)和测试集(test)两部分 + 训练集包含四类apple,orange,banana,mixed(多种水果混合)四类237张图片；测试集包含每类图片各两张。图片集如下图所示。 + 图片类别可由图片名称中提取。训练集图片预览
测试集预览
数据集目录结构
5 部分关键代码
5.1 处理训练集的数据结构import os import pandas as pd train_dir = './Training/' test_dir = './Test/' fruits = [] fruits_image = [] for i in os.listdir(train_dir): for image_filename in os.listdir(train_dir + i): fruits.append(i) # name of the fruit fruits_image.append(i + '/' + image_filename) train_fruits = pd.DataFrame(fruits, columns=["Fruits"]) train_fruits["Fruits Image"] = fruits_image print(train_fruits)
12345678910111213141516 5.2 模型网络结构import matplotlib.pyplot as plt import seaborn as sns from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img from glob import glob from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense img = load_img(train_dir + "Cantaloupe 1/r_234_100.jpg") plt.imshow(img) plt.axis("off") plt.show() array_image = img_to_array(img) # shape (100,100) print("Image Shape --> ", array_image.shape) # 131个类目 fruitCountUnique = glob(train_dir + '/*' ) numberOfClass = len(fruitCountUnique) print("How many different fruits are there --> ",numberOfClass) # 构建模型 model = Sequential() model.add(Conv2D(32,(3,3),input_shape = array_image.shape)) model.add(Activation("relu")) model.add(MaxPooling2D()) model.add(Conv2D(32,(3,3))) model.add(Activation("relu")) model.add(MaxPooling2D()) model.add(Conv2D(64,(3,3))) model.add(Activation("relu")) model.add(MaxPooling2D()) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation("relu")) model.add(Dropout(0.5)) # 区分131类 model.add(Dense(numberOfClass)) # output model.add(Activation("softmax")) model.compile(loss = "categorical_crossentropy", optimizer = "rmsprop", metrics = ["accuracy"]) print("Target Size --> ", array_image.shape[:2])
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647 5.3 训练模型train_datagen = ImageDataGenerator(rescale= 1./255, shear_range = 0.3, horizontal_flip=True, zoom_range = 0.3) test_datagen = ImageDataGenerator(rescale= 1./255) epochs = 100 batch_size = 32 train_generator = train_datagen.flow_from_directory( train_dir, target_size= array_image.shape[:2], batch_size = batch_size, color_mode= "rgb", class_mode= "categorical") test_generator = test_datagen.flow_from_directory( test_dir, target_size= array_image.shape[:2], batch_size = batch_size, color_mode= "rgb", class_mode= "categorical") for data_batch, labels_batch in train_generator: print("data_batch shape --> ",data_batch.shape) print("labels_batch shape --> ",labels_batch.shape) break hist = model.fit_generator( generator = train_generator, steps_per_epoch = 1600 // batch_size, epochs=epochs, validation_data = test_generator, validation_steps = 800 // batch_size) #保存模型 model_fruits.h5 model.save('model_fruits.h5')
123456789101112131415161718192021222324252627282930313233343536顺便输出训练曲线
#展示损失模型结果 plt.figure() plt.plot(hist.history["loss"],label = "Train Loss", color = "black") plt.plot(hist.history["val_loss"],label = "Validation Loss", color = "darkred", linestyle="dashed",markeredgecolor = "purple", markeredgewidth = 2) plt.title("Model Loss", color = "darkred", size = 13) plt.legend() plt.show() #展示精确模型结果 plt.figure() plt.plot(hist.history["accuracy"],label = "Train Accuracy", color = "black") plt.plot(hist.history["val_accuracy"],label = "Validation Accuracy", color = "darkred", linestyle="dashed",markeredgecolor = "purple", markeredgewidth = 2) plt.title("Model Accuracy", color = "darkred", size = 13) plt.legend() plt.show() 123456789101112131415
6 识别效果
from tensorflow.keras.models import load_model import os import pandas as pd from keras.preprocessing.image import ImageDataGenerator,img_to_array, load_img import cv2,matplotlib.pyplot as plt,numpy as np from keras.preprocessing import image train_datagen = ImageDataGenerator(rescale= 1./255, shear_range = 0.3, horizontal_flip=True, zoom_range = 0.3) model = load_model('model_fruits.h5') batch_size = 32 img = load_img("./Test/Apricot/3_100.jpg",target_size=(100,100)) plt.imshow(img) plt.show() array_image = img_to_array(img) array_image = array_image * 1./255 x = np.expand_dims(array_image, axis=0) images = np.vstack([x]) classes = model.predict_classes(images, batch_size=10) print(classes) train_dir = './Training/' train_generator = train_datagen.flow_from_directory( train_dir, target_size= array_image.shape[:2], batch_size = batch_size, color_mode= "rgb", class_mode= "categorical”) print(train_generator.class_indices)
12345678910111213141516171819202122232425262728293031323334
fig = plt.figure(figsize=(16, 16)) axes = [] files = [] predictions = [] true_labels = [] rows = 5 cols = 2 # 随机选择几个图片 def getRandomImage(path, img_width, img_height): """function loads a random image from a random folder in our test path""" folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path))) random_directory = np.random.randint(0, len(folders)) path_class = folders[random_directory] file_path = os.path.join(path, path_class) file_names = [f for f in os.listdir(file_path) if os.path.isfile(os.path.join(file_path, f))] random_file_index = np.random.randint(0, len(file_names)) image_name = file_names[random_file_index] final_path = os.path.join(file_path, image_name) return image.load_img(final_path, target_size = (img_width, img_height)), final_path, path_class def draw_test(name, pred, im, true_label): BLACK = [0, 0, 0] expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 300, cv2.BORDER_CONSTANT, value=BLACK) cv2.putText(expanded_image, "predicted: " + pred, (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.85, (255, 0, 0), 2) cv2.putText(expanded_image, "true: " + true_label, (20, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 255, 0), 2) return expanded_image IMG_ROWS, IMG_COLS = 100, 100 # predicting images for i in range(0, 10): path = "./Test" img, final_path, true_label = getRandomImage(path, IMG_ROWS, IMG_COLS) files.append(final_path) true_labels.append(true_label) x = image.img_to_array(img) x = x * 1./255 x = np.expand_dims(x, axis=0) images = np.vstack([x]) classes = model.predict_classes(images, batch_size=10) predictions.append(classes) class_labels = train_generator.class_indices class_labels = {v: k for k, v in class_labels.items()} class_list = list(class_labels.values()) for i in range(0, len(files)): image = cv2.imread(files[i]) image = draw_test("Prediction", class_labels[predictions[i][0]], image, true_labels[i]) axes.append(fig.add_subplot(rows, cols, i+1)) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) plt.grid(False) plt.axis('off') plt.show()
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