def __init__(self, type):
self.model = enet.build(len(utils.labels), configs.img_height,
configs.img_width)
self.model.load_weights(configs.infer_model_path)
self.backgrounds = self.load_color_backgrounds()
import cv2
import models.enet_naive_upsampling.model as enet
import models.icnet.model as icnet
import numpy as np
import utils
import configs
import matplotlib.pyplot as plt
import time
# image path that you are testing
path = ["./testing_imgs/dirt-road.JPG",
"./testing_imgs/side-walk.JPG"]
m = enet.build(len(utils.labels), configs.img_height, configs.img_width)
m.load_weights("./enet-c-v1-3.h5")
m.summary()
for i in range(len(path)):
org = cv2.imread(path[i])
org = cv2.cvtColor(org, cv2.COLOR_RGB2BGR)
print(org.shape)
image = utils.load_image(path[i])
image = np.array(image, dtype=np.uint8)
start = time.time()
im_mask = m.predict(np.array([image]))[0]
im_mask = utils.convert_class_to_rgb(im_mask)
im_mask = cv2.resize(im_mask, (org.shape[1], org.shape[0]))