def become_dark(path):
to_black_img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
for x in range(to_black_img.shape[0]):
for y in range(to_black_img.shape[1]):
to_black_img[x, y] = 0
cv2.imwrite(path, to_black_img)
print("Loading trained model...")
model = keras.models.load_model("/home/eyue/graduation_desigh_QinJiang/my_model/"
"Concrete_Crack_Classification_model.model")
print("Trained model loaded!")
preprocessd_save_path = img_preprocess.preprocess(preprocess_path, IMAGE_RESIZE)
image_names = [name for name in os.listdir(preprocessd_save_path) for item in IMAGES_FORMAT if
os.path.splitext(name)[1] == item]
# print(os.path.splitext(image_names[0])[0])
for m in range(0, len(image_names)):
every_save_path = os.path.join(preprocessd_save_path, "%s" % os.path.splitext(image_names[m])[0]) # 存储每张图片分割后、重组后、以及评价的目录
os.mkdir(every_save_path)
preprocessd_img = os.path.join(preprocessd_save_path, image_names[m])
print(preprocessd_img)
save_to_every = cv2.imread(preprocessd_img, cv2.IMREAD_GRAYSCALE)
cv2.imwrite(os.path.join(every_save_path, image_names[m]), save_to_every)
cropped_path = crop_and_compose.image_crop(preprocessd_img, every_save_path)
# Init config
if args.model_dir == "":
config = Config(args.model_file, args.params_file)
else:
config = Config(args.model_dir)
config.enable_use_gpu(500, 0)
config.switch_ir_optim()
config.enable_memory_optim()
config.enable_tensorrt_engine(workspace_size=1 << 30, precision_mode=PrecisionType.Float32,max_batch_size=1, min_subgraph_size=5, use_static=False, use_calib_mode=False)
# Create predictor
predictor = create_predictor(config)
# Set input
img = cv2.imread(args.img_path)
img = preprocess(img)
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_handle(input_names[0])
input_tensor.reshape(img.shape)
input_tensor.copy_from_cpu(img.copy())
# Run
predictor.run()
# Set output
output_names = predictor.get_output_names()
output_tensor = predictor.get_output_handle(output_names[0])
output_data = output_tensor.copy_to_cpu()
print("Predict class index: ", np.argmax(output_data))
#!/usr/bin/python3
import os
import argparse
import json
import cv2
from img_preprocess import preprocess
parser = argparse.ArgumentParser()
parser.add_argument("filename",
help="converts image to json request",
type=str)
args = parser.parse_args()
input_file = args.filename
img = preprocess(cv2.imread(input_file))
request = {"instances": img.tolist()}
output_file = os.path.splitext(input_file)[0] + '.json'
with open(output_file, 'w') as out:
json.dump(request, out)
