Merge pull request #25 from hdr-bgnn/Last_dev

Last dev

Scripts/Morphology_main.py

@@ -6,7 +6,7 @@
 @author: thibault
 """
 import Traits_class as tc
-import json, sys
+import json, sys, math
 import numpy as np
 
 def get_scale(metadata_file):
@@ -28,6 +28,28 @@ def get_scale(metadata_file):
         unit =[None]
     return scale , unit
 
+
+def get_angle(metadata_file):
+
+    '''
+    Calculate fish orientation from metadata file using major axis
+    return value in degree
+    
+    '''
+    f = open(metadata_file)
+    data = json.load(f)
+    metadata_fish = list(data.values())[0]['fish'][0]
+
+    major = []
+    length = []
+
+    if 'primary_axis' in metadata_fish  :
+
+        major = metadata_fish['primary_axis']
+        fish_angle = math.atan2(major[1], -major[0])*(180/math.pi)
+
+    return round(fish_angle,2)
+
 # this class is used by json.dump to control that every value as the right format
 # particular problem encounter with np.int64 value type
 class NpEncoder(json.JSONEncoder):
@@ -44,17 +66,35 @@ def default(self, obj):
 def main(input_file, metadata_file, output_measure, output_landmark, output_presence, 
          output_lm_image=None):
 
+    # Create the image segmentation object
     img_seg = tc.segmented_image(input_file)
-    measurement = img_seg.measurement
+    base_name = img_seg.base_name
+    # Calcualte the mesaurements and landmarks
+    img_seg.get_all_measures_landmarks()
+
+    # Assign variables
+    measurements_bbox = img_seg.measurement_with_bbox
+    measurements_lm = img_seg.measurement_with_lm
+    measurements_area = img_seg.measurement_with_area
     landmark = img_seg.landmark
     presence_matrix = img_seg.presence_matrix
 
+    # Combine the 3 types of measurements (lm, bbox, area) and reorder the keys
+    measurement = {'base_name': base_name, **measurements_bbox, **measurements_lm, **measurements_area }
+    list_measure= ['base_name', 'SL_bbox', 'SL_lm', 'HL_bbox', 'HL_lm', 'pOD_bbox', 'pOD_lm', 'ED_bbox', 'ED_lm', 'HH_lm', 'EA_m','HA_m','FA_pca','FA_lm']
+    measurement = {k:measurement[k] for k in list_measure}
+
     # Extract the scale from metadata file
     # and add it to measurement dict
     scale , unit = get_scale(metadata_file)
     measurement['scale'] = scale
     measurement['unit'] = unit                   
 
+    # Extract the fish angle from metadata file
+    # and add it to measurement dict
+    fish_angle = get_angle(metadata_file)
+    measurement['FA_pca_meta'] = fish_angle    
+
     # Save the dictionnaries in json file
     # use NpEncoder to convert the value to correct type (np.int64 -> int)
     with open(output_measure, 'w') as f: