def predict(image_path):
# 先读入图像
image = Image.open(image_path)
# 对图像resize到416,416,3
image = image.resize((416, 416))
image_copy = image.copy()
image_copy = np.array(image_copy)
# 图像转为array
image = np.array(image)
# 图像归一化
image = image / 255
# 弄成批量
image = image.reshape(1, 416, 416, 3)
# 初始化模型
model = mobilenet_segnet(classes_num, 416, 416)
# 模型加载权重
model.load_weights(model_weights_path)
# 进行预测，result = (43264,2)
result = model.predict(image)[0]
# (43264,2->208,208,2)
result = result.reshape(208, 208, classes_num)
# (208,208,2)->(208,208)
result = result.argmax(axis=-1)
seg_img = np.zeros((208, 208, 3))
for c in range(classes_num):
seg_img[:, :, 0] += ((result == c) * colors[c][0]).astype('uint8')
seg_img[:, :, 1] += ((result == c) * colors[c][1]).astype('uint8')
seg_img[:, :, 1] += ((result == c) * colors[c][2]).astype('uint8')
seg_img = Image.fromarray(np.uint8(seg_img)).resize(
(image_copy.shape[0], image_copy.shape[1]))
image_copy = Image.fromarray(image_copy)
image = Image.blend(image_copy, seg_img, 0.5)
image.show()
class_colors = [[0, 0, 0], [0, 255, 0]]
#---------------------------------------------#
# 定义输入图片的高和宽，以及种类数量
#---------------------------------------------#
HEIGHT = 416
WIDTH = 416
#---------------------------------------------#
# 背景 + 斑马线 = 2
#---------------------------------------------#
NCLASSES = 2
#---------------------------------------------#
# 载入模型
#---------------------------------------------#
model = mobilenet_segnet(n_classes=NCLASSES,
input_height=HEIGHT,
input_width=WIDTH)
#--------------------------------------------------#
# 载入权重，训练好的权重会保存在logs文件夹里面
# 我们需要将对应的权重载入
# 修改model_path，将其对应我们训练好的权重即可
# 下面只是一个示例
#--------------------------------------------------#
model.load_weights("logs/ep030-loss0.007-val_loss0.024.h5")
#--------------------------------------------------#
# 对imgs文件夹进行一个遍历
#--------------------------------------------------#
imgs = os.listdir("./img/")
for jpg in imgs:
#--------------------------------------------------#
#---------------------------------------------#
# 该部分用于查看网络结构
#---------------------------------------------#
from nets.segnet import mobilenet_segnet
if __name__ == "__main__":
model = mobilenet_segnet(2, input_height=416, input_width=416)
model.summary()