如何输入用户图像以使用 Tensorflow 进行预测？
How to input user images to predict with Tensorflow?
对于我的项目，我使用 tensorflow 来预测手写用户输入。
基本上我使用了这个数据集：https://www.kaggle.com/rishianand/devanagari-character-set，并创建了一个模型。我使用 matplotlib 查看像素生成的图像。
我的代码基本上适用于训练数据，但我想提高一点。通过 CV2，我创建了一个 GUI，允许用户绘制尼泊尔字母。在此之后，我有一个分支，告诉程序将图像保存在计算机中。
这是我的代码片段：
#creating a forloop to show the image
while True:
img=cv2.imshow('window', win) #showing the window
k= cv2.waitKey(1)
if k==ord('c'):
win= np.zeros((500,500,3), dtype='float64') #creating a new image
#saving the image as a file to then resize it
if k==ord('s'):
cv2.imwrite("nepali_character.jpg", win)
img= cv2.imread("nepali_character.jpg")
cv2.imshow('char', img)
#trying to resize the image using Pillow
size=(32,32)
#create a while loop(make the user print stuff until they print something that STOPS it)
im= Image.open("nepali_character.jpg")
out=im.resize(size)
l= out.save('resized.jpg')
imgout= cv2.imread('resized.jpg')
cv2.imshow("out", imgout)
#finding the pixels of the image, will be printed as a matrix
pix= cv2.imread('resized.jpg', 1)
print(pix)
if k==ord('q'): #if k is 27 then we break the window
cv2.destroyAllWindows()
break
我调整了图像的大小，因为这些是数据集中数据的尺寸。
现在我的问题是如何通过 tensorflow 预测那个字母是什么。
问我老师的时候，他说放到我的数据文件里，然后把它当作训练数据，然后看权重，选最大的权重？
但是我很困惑，我可以把这个图像放到那个数据文件中吗？
如果有人对如何获取用户输入然后进行预测有任何建议，我们将不胜感激
了解数据集：
- 图片尺寸为 32 x 32
- 有46种不同characters/alphabets
['character_10_yna', 'character_11_taamatar', 'character_12_thaa', 'character_13_daa', 'character_14_dhaa', 'character_15_adna', 'character_16_tabala', 'character_17_tha', 'character_18_da', 'character_19_dha', 'character_1_ka', 'character_20_na', 'character_21_pa',
'character_22_pha', 'character_23_ba', 'character_24_bha', 'character_25_ma',
'character_26_yaw', 'character_27_ra', 'character_28_la', 'character_29_waw', 'character_2_kha', 'character_30_motosaw', 'character_31_petchiryakha', 'character_32_patalosaw', 'character_33_ha', 'character_34_chhya',
'character_35_tra', 'character_36_gya', 'character_3_ga', 'character_4_gha', 'character_5_kna', 'character_6_cha', 'character_7_chha', 'character_8_ja',
'character_9_jha', 'digit_0', 'digit_1', 'digit_2', 'digit_3', 'digit_4', 'digit_5', 'digit_6', 'digit_7', 'digit_8', 'digit_9']
因为您的图像在文件夹中分类
因此 keras 实现将是：
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
dataDir = "/xx/xx/xx/xx/datasets/Devanagari/drive-download-20210601T224146Z-001/Train"
data_dir = keras.utils.get_file(dataDir, 'file://'+dataDir)
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
print(image_count)
batch_size = 32
img_height = 180 # scale it up for better performance
img_width = 180 # scale it up for better performance
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names) # 46 classes
用于缓存和规范化
参考 tensorflow tutorial
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
print(np.min(first_image), np.max(first_image))
模型设置编译和训练
num_classes = 46
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
这将导致如下结果（非常有希望！）
Epoch 10/10
1955/1955 [==============================] - 924s 472ms/step - loss: 0.0201 - accuracy: 0.9932 - val_loss: 0.2267 - val_accuracy: 0.9504
保存模型（这需要时间训练，所以最好保存模型）
!mkdir -p saved_model
model.save('saved_model/my_model')
加载模型：
loaded_model = tf.keras.models.load_model('saved_model/my_model')
# Check its architecture
loaded_model.summary()
现在是最后的任务，得到预测。一种方式如下：
import cv2
im2=cv2.imread('datasets/Devanagari/drive-download-20210601T224146Z-001/Test/character_3_ga/3711.png')
im2=cv2.resize(im2, (180,180)) # resize to 180,180 as that is on which model is trained on
print(im2.shape)
img2 = tf.expand_dims(im2, 0) # expand the dims means change shape from (180, 180, 3) to (1, 180, 180, 3)
print(img2.shape)
predictions = loaded_model.predict(img2)
score = tf.nn.softmax(predictions[0]) # # get softmax for each output
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
) # get the np.argmax, means give me the index where probability is max, in this case it got 29. This answers the response
# you got from your instructor. that is "greatest weight"
(180, 180, 3)
(1, 180, 180, 3)
This image most likely belongs to character_3_ga with a 100.00 percent confidence.
另一种方式是通过在线。你想要达到的目标。对于此示例，图像形状需要为 (1, 180, 180, 3)，如果未调整大小，则可以为 (1, 32, 32, 3)。然后喂它进行预测。像下面的东西
out=im.resize(size)
out = tf.expand_dims(out, 0)
predictions = loaded_model.predict(out)
score = tf.nn.softmax(predictions[0]) # # get softmax for each output
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
对于我的项目，我使用 tensorflow 来预测手写用户输入。
基本上我使用了这个数据集：https://www.kaggle.com/rishianand/devanagari-character-set，并创建了一个模型。我使用 matplotlib 查看像素生成的图像。
我的代码基本上适用于训练数据，但我想提高一点。通过 CV2，我创建了一个 GUI，允许用户绘制尼泊尔字母。在此之后，我有一个分支，告诉程序将图像保存在计算机中。
这是我的代码片段：
#creating a forloop to show the image
while True:
img=cv2.imshow('window', win) #showing the window
k= cv2.waitKey(1)
if k==ord('c'):
win= np.zeros((500,500,3), dtype='float64') #creating a new image
#saving the image as a file to then resize it
if k==ord('s'):
cv2.imwrite("nepali_character.jpg", win)
img= cv2.imread("nepali_character.jpg")
cv2.imshow('char', img)
#trying to resize the image using Pillow
size=(32,32)
#create a while loop(make the user print stuff until they print something that STOPS it)
im= Image.open("nepali_character.jpg")
out=im.resize(size)
l= out.save('resized.jpg')
imgout= cv2.imread('resized.jpg')
cv2.imshow("out", imgout)
#finding the pixels of the image, will be printed as a matrix
pix= cv2.imread('resized.jpg', 1)
print(pix)
if k==ord('q'): #if k is 27 then we break the window
cv2.destroyAllWindows()
break
我调整了图像的大小，因为这些是数据集中数据的尺寸。
现在我的问题是如何通过 tensorflow 预测那个字母是什么。
问我老师的时候，他说放到我的数据文件里，然后把它当作训练数据，然后看权重，选最大的权重？
但是我很困惑，我可以把这个图像放到那个数据文件中吗？
如果有人对如何获取用户输入然后进行预测有任何建议，我们将不胜感激
了解数据集：
- 图片尺寸为 32 x 32
- 有46种不同characters/alphabets
['character_10_yna', 'character_11_taamatar', 'character_12_thaa', 'character_13_daa', 'character_14_dhaa', 'character_15_adna', 'character_16_tabala', 'character_17_tha', 'character_18_da', 'character_19_dha', 'character_1_ka', 'character_20_na', 'character_21_pa',
'character_22_pha', 'character_23_ba', 'character_24_bha', 'character_25_ma',
'character_26_yaw', 'character_27_ra', 'character_28_la', 'character_29_waw', 'character_2_kha', 'character_30_motosaw', 'character_31_petchiryakha', 'character_32_patalosaw', 'character_33_ha', 'character_34_chhya',
'character_35_tra', 'character_36_gya', 'character_3_ga', 'character_4_gha', 'character_5_kna', 'character_6_cha', 'character_7_chha', 'character_8_ja',
'character_9_jha', 'digit_0', 'digit_1', 'digit_2', 'digit_3', 'digit_4', 'digit_5', 'digit_6', 'digit_7', 'digit_8', 'digit_9']
因为您的图像在文件夹中分类
因此 keras 实现将是：
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
dataDir = "/xx/xx/xx/xx/datasets/Devanagari/drive-download-20210601T224146Z-001/Train"
data_dir = keras.utils.get_file(dataDir, 'file://'+dataDir)
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
print(image_count)
batch_size = 32
img_height = 180 # scale it up for better performance
img_width = 180 # scale it up for better performance
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names) # 46 classes
用于缓存和规范化 参考 tensorflow tutorial
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
print(np.min(first_image), np.max(first_image))
模型设置编译和训练
num_classes = 46
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
这将导致如下结果（非常有希望！）
Epoch 10/10
1955/1955 [==============================] - 924s 472ms/step - loss: 0.0201 - accuracy: 0.9932 - val_loss: 0.2267 - val_accuracy: 0.9504
保存模型（这需要时间训练，所以最好保存模型）
!mkdir -p saved_model
model.save('saved_model/my_model')
加载模型：
loaded_model = tf.keras.models.load_model('saved_model/my_model')
# Check its architecture
loaded_model.summary()
现在是最后的任务，得到预测。一种方式如下：
import cv2
im2=cv2.imread('datasets/Devanagari/drive-download-20210601T224146Z-001/Test/character_3_ga/3711.png')
im2=cv2.resize(im2, (180,180)) # resize to 180,180 as that is on which model is trained on
print(im2.shape)
img2 = tf.expand_dims(im2, 0) # expand the dims means change shape from (180, 180, 3) to (1, 180, 180, 3)
print(img2.shape)
predictions = loaded_model.predict(img2)
score = tf.nn.softmax(predictions[0]) # # get softmax for each output
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
) # get the np.argmax, means give me the index where probability is max, in this case it got 29. This answers the response
# you got from your instructor. that is "greatest weight"
(180, 180, 3)
(1, 180, 180, 3)
This image most likely belongs to character_3_ga with a 100.00 percent confidence.
另一种方式是通过在线。你想要达到的目标。对于此示例，图像形状需要为 (1, 180, 180, 3)，如果未调整大小，则可以为 (1, 32, 32, 3)。然后喂它进行预测。像下面的东西
out=im.resize(size)
out = tf.expand_dims(out, 0)
predictions = loaded_model.predict(out)
score = tf.nn.softmax(predictions[0]) # # get softmax for each output
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)