def trainConstructModel():
from a_construct_yolo_model import construct_refine_model
model = construct_refine_model()
import b_load_model_weights
b_load_model_weights.load_model_weights(model)
# # (1) 数据探索
# # data explore
# imagePath = './test_images/test1.jpg'
# image = plt.imread(imagePath)
# image_crop = image[300:650,500:,:]
# resized = cv2.resize(image_crop,(448,448))
# import d_inference_image
# out = d_inference_image.inference_image(model, resized)
# # 转换为Channel First
# batch = np.transpose(resized,(2,0,1))
# # 将图像转换到-1到1区间
# batch = 2*(batch/255.) - 1
# # 测试单张图片，需要将一个数据转换为数组batch数据
# batch = np.expand_dims(batch, axis=0)
# label = np.expand_dims(out[0], axis=0)
model.add(Dense(1470))
def dumpBatchImage():
from a_construct_yolo_model import construct_refine_model
model = construct_refine_model()
import b_load_model_weights
b_load_model_weights.load_model_weights(model)
# more examples
images = [plt.imread(file) for file in glob.glob('./test_images/*.jpg')]
batch = np.array([
np.transpose(cv2.resize(image[300:650, 500:, :], (448, 448)),
(2, 0, 1)) for image in images
])
batch = 2 * (batch / 255.) - 1
out = model.predict(batch)
with open(os.path.join("batch_data_6.pkl"), 'wb') as handle:
data = { # Warning: If adding something here, also modifying loadDataset
'x': images,
'y': out
}
pickle.dump(data, handle, -1) # Using the highest protocol available
with open("batch_data_6.pkl", 'rb') as handle:
data = pickle.load(
handle
) # Warning: If adding something here, also modifying saveDataset
batch = data['x']
label = data['y']
def trainConstructModel():
from a_construct_yolo_model import construct_refine_model
model = construct_refine_model()
import b_load_model_weights
b_load_model_weights.load_model_weights(model)
model.add(Dense(1470))
images = []
label = []
with open("batch_data_6.pkl", 'rb') as handle:
data = pickle.load(
handle
) # Warning: If adding something here, also modifying saveDataset
images = data['x']
label = data['y']
batch = np.array([
np.transpose(cv2.resize(image[300:650, 500:, :], (448, 448)),
(2, 0, 1)) for image in images
])
batch = 2 * (batch / 255.) - 1
model.compile(loss='mean_squared_error',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(
batch,
label,
batch_size=1,
nb_epoch=5, # other is 13 is ok
verbose=1,
validation_data=(batch, label))
# model inference the image
import d_inference_image
# # (1) 数据探索
# data explore
imagePath = './test_images/test1.jpg'
image = plt.imread(imagePath)
image_crop = image[300:650, 500:, :]
resized = cv2.resize(image_crop, (448, 448))
out = d_inference_image.inference_image(model, resized)
print("test result")
print(str(out))
# (3) YOLO模型预测结果转换为box坐标
# ineference result convert to box
import e_convert_to_box
boxes = e_convert_to_box.regression_convert_to_box(out)
# (4) 单图像上进行车辆检测
# visualize car detection image results
import f_visualize_image_car_detection
f_visualize_image_car_detection.visualize_image_car_detection(boxes)
def trainConstructModel():
model = construct_refine_model()
y_train = keras.utils.to_categorical(train[1], num_classes)
y_test = keras.utils.to_categorical(test[1], num_classes)
import b_load_model_weights
b_load_model_weights.load_model_weights(model)
# (1) 数据探索
# data explore
import c_visualize_image
resized = c_visualize_image.visualize_images()
import d_inference_image
out = d_inference_image.inference_image(model, resized)
# 转换为Channel First
batch = np.transpose(resized, (2, 0, 1))
# 将图像转换到-1到1区间
batch = 2 * (batch / 255.) - 1
# 测试单张图片，需要将一个数据转换为数组batch数据
batch = np.expand_dims(batch, axis=0)
label = np.expand_dims(out, axis=0)
model.compile(loss='mean_squared_error',
optimizer='adadelta',
metrics=['accuracy'])
t = now()
model.fit(batch,
label,
batch_size=batch_size,
epochs=1,
verbose=1,
validation_data=(x_test, y_test))
#construct_yolo_model()
from keras.models import Sequential from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.core import Flatten, Dense, Activation, Reshape from utils import load_weights, Box, yolo_net_out_to_car_boxes, draw_box import pylab # (0) 构建YOLO实时检测模型 # construct the tiny-yolo model import a_construct_yolo_model model = a_construct_yolo_model.construct_yolo_model() import b_load_model_weights b_load_model_weights.load_model_weights(model) # (1) 数据探索 # data explore import c_visualize_image resized = c_visualize_image.visualize_images() # (2) 图像模型推断 # model inference the image import d_inference_image out = d_inference_image.inference_image(model, resized) # (3) YOLO模型预测结果转换为box坐标 # ineference result convert to box
