'model_file': os.path.join(model_dir, 'InceptionResnetV2-004-0.984.hdf5'), 'input_shape': (299, 299, 3), 'model_weight': 1 } dicts_models.append(dict_model1) dict_model1 = { 'model_file': os.path.join(model_dir, 'Xception-004-0.984.hdf5'), 'input_shape': (299, 299, 3), 'model_weight': 1 } dicts_models.append(dict_model1) filename_csv = os.path.join(dir_dest, 'LaserSpot_predict_dir.csv') if GEN_CSV: os.makedirs(os.path.dirname(filename_csv), exist_ok=True) write_csv_dir_nolabel(filename_csv, dir_preprocess) df = pd.read_csv(filename_csv) all_files, all_labels = get_images_labels(filename_csv_or_pd=df) prob_total, y_pred_total, prob_list, pred_list = \ do_predict(dicts_models, filename_csv, argmax=True) import pickle os.makedirs(os.path.dirname(pkl_prob), exist_ok=True) with open(pkl_prob, 'wb') as file: pickle.dump(prob_total, file) if COMPUTE_DIR_FILES: op_files_multiclass(filename_csv, prob_total,
crop_optic_disc_dir(dir_source=dir_preprocess512, dir_dest=dir_crop_optic_disc, server_port=21000, mask=True) print('crop optic disc 112 OK!') if GEN_CSV: if GET_LABELS_FROM_DIR: dict_mapping = {} for i in range(30): dict_mapping[str(i)] = str(i) my_data.write_csv_based_on_dir(filename_csv, dir_crop_optic_disc, dict_mapping) else: my_data.write_csv_dir_nolabel(filename_csv, dir_crop_optic_disc) df = pd.read_csv(filename_csv) all_files, all_labels = my_data.get_images_labels(filename_csv_or_pd=df) prob_total, y_pred_total, prob_list, pred_list =\ LIBS.DLP.my_predict_helper.do_predict_batch(dicts_models, filename_csv, gpu_num=GPU_NUM) import pickle if not os.path.exists(os.path.dirname(pkl_prob)): os.makedirs(os.path.dirname(pkl_prob)) with open(pkl_prob, 'wb') as file: pickle.dump(prob_total, file) # pkl_file = open(pkl_prob, 'rb') # prob_total = pickle.load(pkl_file)
COMPUTE_DIR_FILES = True dir_original = '/media/ubuntu/data1/ROP项目/人机比赛用图_20200317/original/三标签' dir_blood_vessel_seg = '/media/ubuntu/data1/ROP项目/人机比赛用图_20200317/results/Plus/blood_vessel_seg' dir_dest = '/media/ubuntu/data1/ROP项目/人机比赛用图_20200317/results_2020_5_20/Plus/result_2020_5_20' # dir_original = '/tmp5/ROP_human_AI/mydataset/正常/original' # dir_blood_vessel_seg = '/tmp5/ROP_human_AI/mydataset/正常/blood_vessel_seg_result' # dir_dest = '/tmp5/ROP_human_AI/mydataset/正常/Plus_two_steps' pkl_prob = os.path.join(dir_dest, 'probs.pkl') filename_csv = os.path.join(dir_dest, 'plus_two_stage_results.csv') if GEN_CSV: os.makedirs(os.path.dirname(filename_csv), exist_ok=True) write_csv_dir_nolabel(filename_csv, dir_blood_vessel_seg) # dicts_models = [] # model_file1 = '/home/ubuntu/dlp/deploy_models/ROP/plus_two_stages/2020_4_28/InceptionResnetV2-008-0.973.hdf5' # dict_model1 = {'model_file': model_file1, # 'input_shape': (299, 299, 3), 'model_weight': 1} # dicts_models.append(dict_model1) # model_file1 = '/home/ubuntu/dlp/deploy_models/ROP/plus_two_stages/2020_4_28/InceptionV3-006-0.969.hdf5' # dict_model1 = {'model_file': model_file1, # 'input_shape': (299, 299, 3), 'model_weight': 1} # dicts_models.append(dict_model1) # model_file1 = '/home/ubuntu/dlp/deploy_models/ROP/plus_two_stages/2020_4_28/Xception-005-0.967.hdf5' # dict_model1 = {'model_file': model_file1, # 'input_shape': (299, 299, 3), 'model_weight': 1} # dicts_models.append(dict_model1)
from LIBS.ImgPreprocess import my_preprocess_dir image_size = 512 my_preprocess_dir.do_process_dir(dir_original, dir_preprocess, image_size=image_size) if GEN_CSV: if not os.path.exists(os.path.dirname(filename_csv)): os.makedirs(os.path.dirname(filename_csv)) if GET_LABELS_FROM_DIR: dict_mapping = {} for i in range(30): dict_mapping[str(i)] = str(i) my_data.write_csv_based_on_dir(filename_csv, dir_preprocess, dict_mapping) else: my_data.write_csv_dir_nolabel(filename_csv, dir_preprocess) df = pd.read_csv(filename_csv) all_files, all_labels = my_data.get_images_labels(filename_csv_or_pd=df) prob_total, y_pred_total, prob_list, pred_list =\ LIBS.DLP.my_predict_helper.do_predict_batch(dicts_models, filename_csv, gpu_num=GPU_NUM) if not os.path.exists(os.path.dirname(pkl_prob)): os.makedirs(os.path.dirname(pkl_prob)) with open(pkl_prob, 'wb') as file: pickle.dump(prob_total, file) # pkl_file = open(prob_pkl', 'rb')
DIR_PREPROCESS = '/media/ubuntu/data1/糖网项目/DR分级英国标准_20190119_无杂病/DR/preprocess384/' DIR_DEST = '/media/ubuntu/data1/糖网项目/DR分级英国标准_20190119_无杂病/DR/results/CAM/' from LIBS.ImgPreprocess import my_preprocess_dir if DO_PREPROCESS: my_preprocess_dir.do_preprocess_dir(DIR_ORIGINAL, DIR_PREPROCESS, image_size=384, is_rop=False, add_black_pixel_ratio=0.07) filename_csv = os.path.join(DIR_DEST, 'csv', 'predict_dir.csv') if GEN_CSV: os.makedirs(os.path.dirname(filename_csv), exist_ok=True) from LIBS.DataPreprocess.my_data import write_csv_dir_nolabel write_csv_dir_nolabel(filename_csv, DIR_PREPROCESS) #region load and convert models model_dir = '/tmp5/models_2020_6_19/DR_english/v1' dicts_models = [] dict_model1 = { 'model_file': os.path.join(model_dir, 'InceptionResnetV2-004-0.984.hdf5'), 'input_shape': (299, 299, 3), 'model_weight': 1 } dicts_models.append(dict_model1) for dict1 in dicts_models: print('prepare to load model:' + dict1['model_file']) dict1['model'] = keras.models.load_model(dict1['model_file'],