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infer_cloud_fraction.py 1.32 KiB
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from util.setup_cloud_fraction import model_path
from aeolus.datasource import CLAVRx
import os
from deeplearning.cloud_fraction_fcn_abi import SRCNN, run_evaluate_static, run_evaluate_static_full_disk


def infer_cloud_fraction(clvrx_path, output_dir, full_disk=True):
    ckpt_dir_s = os.listdir(model_path)
    ckpt_dir = model_path + ckpt_dir_s[0]

    clvrx_ds = CLAVRx(clvrx_path)
    for fname, t_start, t_stop in clvrx_ds:
        dto = clvrx_ds.get_datetime(fname)
        clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
        out_file = output_dir + 'cloud_fraction_' + clvrx_str_time
        if full_disk:
            run_evaluate_static_full_disk(fname, out_file, ckpt_dir)
        else:
            run_evaluate_static(fname, out_file, ckpt_dir)


def infer_cloud_fraction_new(clvrx_path, output_dir, full_disk=True):
    ckpt_dir_s = os.listdir(model_path)
    ckpt_dir = model_path + ckpt_dir_s[0]

    nn = SRCNN()
    nn.setup_inference(ckpt_dir)

    clvrx_ds = CLAVRx(clvrx_path)
    for fname, t_start, t_stop in clvrx_ds:
        dto = clvrx_ds.get_datetime(fname)
        clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
        out_file = output_dir + 'cloud_fraction_' + clvrx_str_time
        if full_disk:
            nn.run_inference_full_disk(fname, out_file)
        else:
            nn.run_inference(fname, out_file)