updated README and data preprocessing

README.md

@@ -49,14 +49,20 @@ which segments the input image into patches of 256*256, seperately perform CR re
 
 ### Currently available models
 
-mask: ACS-WFC-F606W-2-4
-      ACS-WFC-F606W-2-32(*)
+mask:
+    ACS-WFC-F606W-2-4
+    ACS-WFC-F606W-2-32(*)
 
-inpaint: ACS-WFC-F606W-2-32
-         ACS-WFC-F606W-3-32(*)
+inpaint:
+    ACS-WFC-F606W-2-32
+    ACS-WFC-F606W-3-32(*)
 
 The two numbers following instrument configuration specifies model size, with larger number indicating better performing model at the expense of runtime. Recommanded models are marked in (*). For benchmarking of these models, please refer to the original paper.
 
+### API Documentation
+
+Documentation is under development at: https://deepcr.readthedocs.io/en/latest/deepCR.html
+
 ### Limitations and Caveats
 
 In the current release, the included models have been built and tested only on Hubble Space Telescope (HST) ACS/WFC images in the F606W filter. Application to native-spatial resolution (ie. not drizzled), calibrated images from ACS/F606W (`*_flc.fits`) is expected to work well. Use of these prepackaged models in other observing modes with HST or spectroscopy is not encouraged. We are planning hosting a "model zoo" that would allow deepCR to be adapted to a wide range of instrument configurations.

deepCR/model.py

@@ -2,7 +2,6 @@
 """
 from os import path, mkdir
 import math
-
 import numpy as np
 import joblib
 import torch
@@ -24,7 +23,7 @@
 
 class deepCR():
 
-    def __init__(self, mask=None, inpaint='medmask', device='CPU',
+    def __init__(self, mask='ACS-WFC-F606W-2-32', inpaint='medmask', device='CPU',
                  model_dir=default_model_path):
 
         """model class instantiation for deepCR. Here is the
@@ -34,7 +33,7 @@ def __init__(self, mask=None, inpaint='medmask', device='CPU',
     Parameters
     ----------
     mask : str
-        Name of the mask to use. This is one of the keys in
+        Name of deepCR-mask model to use. This is one of the keys in
         `mask_dict`
     inpaint : str
         Name of the inpainting model to use. This is one of the keys in
@@ -89,17 +88,14 @@ def __init__(self, mask=None, inpaint='medmask', device='CPU',
     def clean(self, img0, threshold=0.5, inpaint=True, binary=True,
               seg=0, parallel=False, n_jobs=-1):
         """
-            helper function to pass img0 to either
-            self.clean_single()
-            or
-            self.clean_seg()
-        :param img0 (np.ndarray): 2D input image
-        :param threshold: for creating binary mask from probablistic mask
-        :param inpaint: return clean image only if True
-        :param binary: return binary mask if True. probabilistic mask otherwise.
-        :param seg: blocksize to apply models on
+            Identify cosmic rays in an input image, and (optionally) inpaint with the predicted cosmic ray mask
+        :param img0: (np.ndarray) 2D input image conforming to model requirements. For HST ACS/WFC, must be from _flc.fits and in units of electrons in native resolution.
+        :param threshold: (float) applied to probabilistic mask to generate binary mask
+        :param inpaint: (bool) return clean, inpainted image only if True
+        :param binary: return binary CR mask if True. probabilistic mask if False
+        :param seg: for large input images, blocksize to apply models on
         :param parallel: run in parallel if True and seg > 0
-        :param n_jobs number of jobs to run in parallel, passed to `joblib`
+        :param n_jobs: number of jobs to run in parallel, passed to `joblib`
         :return: mask or binary mask; or None if internal call
         """
         if seg==0:
@@ -284,6 +280,8 @@ def inpaint(self, img0, mask):
         :param mask (np.ndarray): 2D input mask
         :return: inpainted image
         """
+        img0 = img0.astype(np.float32) / 100
+        mask = mask.astype(np.float32)
         shape = img0.shape[-2:]
         if self.inpaintNet is not None:
             img0 = from_numpy(img0).type(self.dtype). \
@@ -299,6 +297,5 @@ def inpaint(self, img0, mask):
         else:
             img1 = medmask(img0, mask)
             inpainted = img1 * mask + img0 * (1 - mask)
-
-        return inpainted
+        return inpainted * 100