Updated the dependencies file

Author: nholschuh

Diff for: dependencies.txt

@@ -1,6 +1,28 @@
+IPython
+PIL
 astropy
-hdf5storage
+cv2
+datetime
 ezdxf
-mat73
+geopandas
+glob
+h5py
+hdf5storage
 imageio
-ffmpeg-python
+inspect
+mat73
+matplotlib
+numpy
+os
+pandas
+pdb
+pyproj
+requests
+scipy
+shapely
+subprocess
+sys
+time
+tqdm
+warnings
+xarray

Diff for: edgetrim_mask.py

@@ -1,6 +1,6 @@
 
 
-def edgetrim_mask(edge_trim_array, debug_flag=0, start_trim=0, end_trim=0):
+def edgetrim_mask(edge_trim_array, debug_flag=0, start_trim=0, end_trim=0, additional_narrowing=0):
     """
     % (C) Nick Holschuh - Amherst College -- 2022 ([email protected])
     %
@@ -23,20 +23,31 @@ def edgetrim_mask(edge_trim_array, debug_flag=0, start_trim=0, end_trim=0):
     from scipy.interpolate import interp1d
     mask = np.zeros(edge_trim_array.shape)
 
+
+    ###################################################################
+    ### Here we identify the non-NAN values in the edge_trim matrix ###
+    ###################################################################
     et_row,et_col = np.where(~np.isnan(edge_trim_array))
     center_split = np.mean(et_row)
     left_ind = np.where(et_row < center_split)
     right_ind = np.where(et_row >= center_split)
 
 
+    ###################################################################
+    ### We interpolate the edge_trim values across all slices      ####
+    ###################################################################
     col_opts = np.arange(0,edge_trim_array.shape[1])
 
-    left_row_interpolator = interp1d(et_col[left_ind],et_row[left_ind],fill_value=np.NaN,bounds_error=0)
-    right_row_interpolator = interp1d(et_col[right_ind],et_row[right_ind],fill_value=np.NaN,bounds_error=0)
+    left_row_interpolator = interp1d(et_col[left_ind],et_row[left_ind]+additional_narrowing,fill_value=np.NaN,bounds_error=0)
+    right_row_interpolator = interp1d(et_col[right_ind],et_row[right_ind]-additional_narrowing,fill_value=np.NaN,bounds_error=0)
 
     left_row = left_row_interpolator(col_opts).astype(int)
     right_row = right_row_interpolator(col_opts).astype(int)
 
+    
+    ###################################################################
+    ### Then we build the mask from the interpolated values        ####
+    ###################################################################
     for i in col_opts:
         mask[left_row[i]:right_row[i]+1,i] = 1
     for i in range(start_trim):

Diff for: find_pixelcoords.py

@@ -55,8 +55,8 @@ def find_pixelcoords(im_filename,original_width,original_height,im_pick_params=0
         rinds = ndh.minmax(im_frame[0])
     else:
     ########################## This is meant to handle some spillover of lines and points outside the image
-        row_sum = np.sum(np_frame[:,:,3] == 255,axis=1)
-        col_sum = np.sum(np_frame[:,:,3] == 255,axis=0)
+        row_sum = np.sum(np_frame[:,:,2] == 255,axis=1)
+        col_sum = np.sum(np_frame[:,:,2] == 255,axis=0)
 
         rinds = ndh.minmax(np.where(row_sum > np.mean(row_sum)))
         cinds = ndh.minmax(np.where(col_sum > np.mean(col_sum)))
@@ -113,10 +113,11 @@ def find_pixelcoords(im_filename,original_width,original_height,im_pick_params=0
                     pick_temp_temp = []
                     for ind3, cols in enumerate(np.unique(true_x_inds)):
                         true_x_ind = int(cols) 
+                        compare_inds = np.where(true_x_inds == cols)[0]
                         ########## We want to lean toward the top side, so we split the difference between mean and min
                         y_ind = int(np.mean([
-                            np.mean(rind[true_x_inds == cols]),
-                            np.min(rind[true_x_inds == cols])])
+                            np.mean(rind[compare_inds]),
+                            np.min(rind[compare_inds])])
                         )
                         true_y_ind = np.round(yrange[y_ind])                        
                         pick_temp_temp.append([true_x_ind,true_y_ind])