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基于Python的水果分类训练代码和识别代码和gui界面(支持VGG16，googlenet, resnet, inception, mobilenet)_图像识别水果 python
作者：2023面试高手 | 2024-05-29 13:34:54
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图像识别水果 python
基于Python的水果分类训练代码和识别代码和gui界面(支持VGG16，googlenet, resnet, inception, mobilenet)
设计项目代码案例地址，一对一指导设计，疑难解答，代码安装部署：咨询链接 问题
有一组水果的训练集，我们对模型进行训练，思路跟之前我们识别猫与狗一样。
设计解决这个问题的思路
1、下载与放置训练图片
2、现在对应的依赖，tensorflow、numpy等等
3、构建训练集合
4、建模
5、对模型进行训练
6、用测试模型进行验证
7、输出结果
8、优化模型 to step4
[1]
图片地址
https://www.kaggle.com/moltean/fruits 现在数据，现在速度比较慢，可以使用网盘。
网盘地址(提取码:a9wr)
【2】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
-
-
- 1
结果输出
-
Fruits Fruits Image
-
0 Tomato
4 Tomato
4
/r_
236_
100.jpg
-
1 Tomato
4 Tomato
4
/
247_
100.jpg
-
2 Tomato
4 Tomato
4
/
257_
100.jpg
-
3 Tomato
4 Tomato
4
/r_
78_
100.jpg
-
4 Tomato
4 Tomato
4
/r_
68_
100.jpg
-
... ... ...
-
67687 Peach Flat Peach Flat
/
220_
100.jpg
-
67688 Peach Flat Peach Flat
/r_
127_
100.jpg
-
67689 Peach Flat Peach Flat
/
156_
100.jpg
-
67690 Peach Flat Peach Flat
/r_
137_
100.jpg
-
67691 Peach Flat Peach Flat
/
146_
100.jpg
- 1
【3】构造模型
-
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])
- 1
【4】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')
- 1
【5】展示训练结果
-
#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()
- 1
验证
新建一个文件，用于验证训练出来的模型
-
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)
- 1
输出结果
-
[
13]
-
Found
67692 images belonging
to
131 classes.
-
{
'Apple Braeburn':
0,
'Apple Crimson Snow':
1,
'Apple Golden 1':
2,
'Apple Golden 2':
3,
'Apple Golden 3':
4,
'Apple Granny Smith':
5,
'Apple Pink Lady':
6,
'Apple Red 1':
7,
'Apple Red 2':
8,
'Apple Red 3':
9,
'Apple Red Delicious':
10,
'Apple Red Yellow 1':
11,
'Apple Red Yellow 2':
12,
'Apricot':
13,
'Avocado':
14,
'Avocado ripe':
15,
'Banana':
16,
'Banana Lady Finger':
17,
'Banana Red':
18,
'Beetroot':
19,
'Blueberry':
20,
'Cactus fruit':
21,
'Cantaloupe 1':
22,
'Cantaloupe 2':
23,
'Carambula':
24,
'Cauliflower':
25,
'Cherry 1':
26,
'Cherry 2':
27,
'Cherry Rainier':
28,
'Cherry Wax Black':
29,
'Cherry Wax Red':
30,
'Cherry Wax Yellow':
31,
'Chestnut':
32,
'Clementine':
33,
'Cocos':
34,
'Corn':
35,
'Corn Husk':
36,
'Cucumber Ripe':
37,
'Cucumber Ripe 2':
38,
'Dates':
39,
'Eggplant':
40,
'Fig':
41,
'Ginger Root':
42,
'Granadilla':
43,
'Grape Blue':
44,
'Grape Pink':
45,
'Grape White':
46,
'Grape White 2':
47,
'Grape White 3':
48,
'Grape White 4':
49,
'Grapefruit Pink':
50,
'Grapefruit White':
51,
'Guava':
52,
'Hazelnut':
53,
'Huckleberry':
54,
'Kaki':
55,
'Kiwi':
56,
'Kohlrabi':
57,
'Kumquats':
58,
'Lemon':
59,
'Lemon Meyer':
60,
'Limes':
61,
'Lychee':
62,
'Mandarine':
63,
'Mango':
64,
'Mango Red':
65,
'Mangostan':
66,
'Maracuja':
67,
'Melon Piel de Sapo':
68,
'Mulberry':
69,
'Nectarine':
70,
'Nectarine Flat':
71,
'Nut Forest':
72,
'Nut Pecan':
73,
'Onion Red':
74,
'Onion Red Peeled':
75,
'Onion White':
76,
'Orange':
77,
'Papaya':
78,
'Passion Fruit':
79,
'Peach':
80,
'Peach 2':
81,
'Peach Flat':
82,
'Pear':
83,
'Pear 2':
84,
'Pear Abate':
85,
'Pear Forelle':
86,
'Pear Kaiser':
87,
'Pear Monster':
88,
'Pear Red':
89,
'Pear Stone':
90,
'Pear Williams':
91,
'Pepino':
92,
'Pepper Green':
93,
'Pepper Orange':
94,
'Pepper Red':
95,
'Pepper Yellow':
96,
'Physalis':
97,
'Physalis with Husk':
98,
'Pineapple':
99,
'Pineapple Mini':
100,
'Pitahaya Red':
101,
'Plum':
102,
'Plum 2':
103,
'Plum 3':
104,
'Pomegranate':
105,
'Pomelo Sweetie':
106,
'Potato Red':
107,
'Potato Red Washed':
108,
'Potato Sweet':
109,
'Potato White':
110,
'Quince':
111,
'Rambutan':
112,
'Raspberry':
113,
'Redcurrant':
114,
'Salak':
115,
'Strawberry':
116,
'Strawberry Wedge':
117,
'Tamarillo':
118,
'Tangelo':
119,
'Tomato 1':
120,
'Tomato 2':
121,
'Tomato 3':
122,
'Tomato 4':
123,
'Tomato Cherry Red':
124,
'Tomato Heart':
125,
'Tomato Maroon':
126,
'Tomato Yellow':
127,
'Tomato not Ripened':
128,
'Walnut':
129,
'Watermelon':
130}
- 1
识别出是13：Apricot
进一步验证
-
-
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()
- 1
呈现结果
基于Python，MATLAB设计，OpenCV,CNN,机器学习,R-CNN,GCN,LSTM,SVM,BP神经网络，数字识别,贝叶斯,逻辑回归,卷积神经网络等算法的中文文本分类.车牌识别,知识图谱,数字图像处理,手势识别,边缘检测，图像增强,图像分类,图像分割，色彩增强，低照度。缺陷检测，病害识别，图像缺陷检测，图像去噪，去雾，去模糊，目标检测，图像分类，图像分割，语义分割等代做，代码跑通，小目标检测，网络优化，注意力机制，调参等等系
这些题目旨在涵盖深度学习图像方面的广泛应用领域，帮助本科毕业生选择适合自己兴趣和目标的毕业设计课题。学生可以根据自己的研究兴趣和可用资源来进一步调整和细化这些题目。
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