虚拟现实和增强现实之数据处理算法：深度学习：虚拟现实与增强现实的未来趋势.docx

import tensorflow as tf

from tensorflow.keras.preprocessing import image

from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions

import numpy as np

model = tf.keras.applications.ResNet50(weights='imagenet')

img_path = 'path_to_your_image.jpg'

img = image.load_img(img_path, target_size=(224, 224))

x = image.img_to_array(img)

model_path = 'path_to_your_model'

encoder_path = os.path.join(model_path, "encoder.pth")

depth_decoder_path = os.path.join(model_path, "depth.pth")

encoder = ResnetEncoder(18, False)

depth_decoder = DepthDecoder(num_ch_enc=encoder.num_ch_enc, scales=range(4))

loaded_dict_enc = torch.load(encoder_path, map_location='cpu')

loaded_dict_dec = torch.load(depth_decoder_path, map_location='cpu')

input_image = np.expand_dims(input_image, axis=0)

input_image = preprocess_input(input_image)

features = encoder(torch.tensor(input_image).float())

outputs = depth_decoder(features)

disp = outputs[("disp", 0)]

disp_resized = torch.nn.functional.interpolate(disp, (input_image.shape[1], input_image.shape[2

]), mode="bilinear", align_corners=False)

import numpy as np

total = np.dot(self.weights, inputs) + self.bias

weights = np.array([0, 1]) #

bias = 4 #

n = Neuron(weights, bias)

inputs = np.array([2, 3])

output = n.feedforward(inputs)

print(output)

import keras

from keras.models import Sequential

from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D

model = Sequential()