网络摄像头框架到model.predict_generator - Jupyter Notebook & CV2

Question

我已经创建了这个自定义CNN，并对其进行了训练，现在希望尝试实时传递来自我的网络摄像头的帧以测试预测。

网络摄像头视频播放开始逐帧捕捉，然而，我不确定要对帧做什么才能使其与CNN模型一起工作

如有任何建议，不胜感激

我已经提供了我想要实现的完整代码

#imported necessities
import os
import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
import cv2
from matplotlib.image import imread
from IPython.display import clear_output
import time
import PIL.Image
from io import StringIO
import IPython.display
import numpy as np
from io import BytesIO
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Conv2D, MaxPool2D, Dropout, Flatten, MaxPooling2D
from tensorflow.keras.callbacks import EarlyStopping
#Data Paths
data_dir = 'C:\Users\User\Desktop\DATAWeather'
test_path = data_dir+'\Test\'
train_path = data_dir+'\Train\'
#Variable to resize all of the images
image_shape = (224,224,3) #224*224*3 = 150528 Data Points : thats why we need image batch
#Apply a generator so it does not always get the same format of picture (recognizes different things)
image_gen = ImageDataGenerator(rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, rescale=1/255, shear_range=0.1, zoom_range=0.1,horizontal_flip=True,fill_mode='nearest')
#setting up a base convolutional layer
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_shape, activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(4))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam', metrics=['accuracy']), #model.summary()
#Create an early EPOCH stoppage based on the validation loss based off TWO epochs 
early_stop=EarlyStopping(monitor='val_loss', patience=2)
#TRAINING MODEL - use two to the power 
batch_size=32
#TWO generators 
train_image_gen = image_gen.flow_from_directory(train_path, target_size=image_shape[:2], color_mode='rgb', batch_size = batch_size, class_mode='categorical', shuffle=True)
test_image_gen = image_gen.flow_from_directory(test_path, target_size=image_shape[:2], color_mode='rgb', batch_size = batch_size, class_mode='categorical', shuffle=False)
results = model.fit_generator(train_image_gen, epochs=1, validation_data=test_image_gen, callbacks=[early_stop])

***
**def showarray(a, fmt='jpeg'):
f = BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
IPython.display.display(IPython.display.Image(data=f.getvalue()))

def get_frame(cam):
# Capture frame-by-frame
ret, frame = cam.read()

#flip image for natural viewing
frame = cv2.flip(frame, 1)

return frame

cam = cv2.VideoCapture(0)
def make_1080p():
cam.set(3, 224)
cam.set(4, 224)
def change_res(width, height):
cam.set(3, width)
cam.set(4, height)
change_res(224, 224)
try:
while(True):
t1 = time.time()
frame = get_frame(cam)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
showarray(frame)
t2 = time.time()
print("%f FPS" % (1/(t2-t1)))
# Display the frame until new frame is available
clear_output(wait=True)
Weather_Prediction_Cell = (frame)
#Weather_Prediction_Cell /= 255
model.predict_generator(frame)
#print(Weather_Prediction_Cell)
print(pred)**


except KeyboardInterrupt:
cam.release()
print("Stream stopped")
***

Answer 1

Keras模型有一个叫做"predict"的方法。它需要一个np。数组或np的列表。数组作为输入(它应该与你的神经网络具有完全相同的形状)。输入，包括批处理部分(例如batch_count, width, height, channels)。你把这个输入到模型中。预测，然后它将结果作为np返回给你。数组，用你的神经网络输出层的形状。我不习惯使用opencv的网络摄像头应用程序，但是如果你在np中获得帧数据。数组，你可以把它输入你的神经网络。也只要确定它的形状，并在需要时重塑它。