update name for the measurment

notebook update + new test_Data

Update README.md

fix error .json serialization... in Moprhology_main.py

Update README.md

remove cpython-38.pyc

remove __pycache

README.md

@@ -102,6 +102,11 @@ img_seg = tc.segmented_image("image_segmented.png")
 
 ## 4- Input and Output
 The main script is Morphology_main.py The usage is python Morphology_main.py  input_file.png metadata.json measure.json landmark.json presence.json image_lm.png
+Code to test using the data provided in [/Test_Data/]()
+```
+Morphology_main.py Test_Data/INHS_FISH_18609_segmented.png Test_Data/INHS_FISH_18609.js
+on Test_Data/INHS_FISH_18609_measure.json Test_Data/INHS_FISH_18609_landmark.json Test_Data/INHS_FISH_18609_presence.json Test_Data/INHS_FISH_18609_image_lm.png
+```
 
  + input_file.png : segmented fish image generated by [Maruf code](), [example here](https://github.com/thibaulttabarin/Morphology-analysis/blob/main/Test_Data/INHS_FISH_000742_segmented.png)
  + metadata.json : generated by [drexel]() [example here](https://github.com/thibaulttabarin/Morphology-analysis/blob/main/Test_Data/INHS_FISH_000742.json)
@@ -120,6 +125,26 @@ You will need to use [Morphology_env.yml](https://github.com/thibaulttabarin/Mor
 We use github action to create a container what run the main script [Morphology_main.py](https://github.com/thibaulttabarin/Morphology-analysis/blob/main/Scripts/Morphology_main.py). 
   1. The workflow to build the container is defined [here](https://github.com/thibaulttabarin/Morphology-analysis/blob/main/.github/workflows/Deploy_Morpholgy.yml).   
   2. The Dockerfile definition is [here](https://github.com/thibaulttabarin/Morphology-analysis/blob/main/Dockerfile)
-  3. Pull command : docker pull ghcr.io/thibaulttabarin/morphology-analysis/morphology:0.0.2 or singularity pull My_morphology_name.sif
-  4. To access the instruction Run : "singularity run My_morphology_name.sif" or 
-  5. Usage : singularity exec My_morphology_name.sif Morphology_main.py  <input_file> <metadata.json> <measure.json> <landmark.json> <presence.json> <image_lm.png>
+  3. Pull command : 
+  ```
+  docker pull ghcr.io/thibaulttabarin/morphology-analysis/morphology:latest
+  #or 
+  singularity pull My_morphology_name.sif docker://ghcr.io/thibaulttabarin/morphology-analysis/morphology:latest
+  ```
+  4. To access the instruction Run : "
+  ```
+  singularity run My_morphology_name.sif
+  ```
+  5. Usage : 
+  ```
+  singularity exec My_morphology_name.sif Morphology_main.py  <input_file> <metadata.json> <measure.json> <landmark.json> <presence.json> <image_lm.png>
+  # test with
+  singularity exec My_morphology_name.sif Morphology_main.py Test_Data/INHS_FISH_18609_segmented.png Test_Data/INHS_FISH_18609.json   Test_Data/INHS_FISH_18609_measure.json Test_Data/INHS_FISH_18609_landmark.json Test_Data/INHS_FISH_18609_presence.json Test_Data/INHS_FISH_18609_image_lm.png
+  ```
+## 7- Development tricks
+
+If you want to test neww version of Morphology_main.py (upudated version on your local computer) you can use the container by bind the local folder (here, Scripts/) containing the updated version of Morphology_main.py and /pipeline/Morphology is where Morphology_main.py is expected to be in the container.
+```
+singularity exec --bind Scripts/:/pipeline/Morphology morpho.sif Morphology_main.py Test_Data/INHS_FISH_18609_segmented.png Test_Data/INHS_FISH_18609.json Test_Data/INHS_FISH_18609_measure.json Test_Data/INHS_FISH_18609_landmark.json Test_Data/INHS_FISH_18609_presence.json Test_Data/INHS_FISH_18609_image_lm.png
+
+```

Scripts/Morphology_main.py

@@ -7,6 +7,7 @@
 """
 import Traits_class as tc
 import json, sys
+import numpy as np
 
 def get_scale(metadata_file):
 
@@ -27,6 +28,18 @@ def get_scale(metadata_file):
         unit =[None]
     return scale , unit
 
+# 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):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            print(obj)
+            return float(obj)
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)
 
 def main(input_file, metadata_file, output_measure, output_landmark, output_presence, 
          output_lm_image=None):
@@ -43,8 +56,9 @@ def main(input_file, metadata_file, output_measure, output_landmark, output_pres
     measurement['unit'] = unit                   
 
     # 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:
-        json.dump(measurement, f)    
+        json.dump(measurement, f, cls=NpEncoder)    
 
     with open(output_landmark, 'w') as f:
         json.dump(landmark, f)

Scripts/Traits_class.py

@@ -362,27 +362,47 @@ def all_landmark(self):
 
         # reorder the key 
         new_landmark={}
-        list_order = [str(i) for i in range(1,19)]
+        list_order = [str(i) for i in range(1,16)]
         for key in list_order:
             new_landmark[key] = landmark[key]                     
 
         return new_landmark
 
 
+    def measure_eye_area(self):
+        '''
+        Calculate eye area after cleaning and filing hole
+        
+        '''
+        mask = self.mask
+        eye_region = self.clean_trait_region(mask['eye'])
+
+        return eye_region.area
+
+    def measure_head_area(self):
+        '''
+        Calculate head area  after cleaning and filing hole
+        
+        '''
+        mask = self.mask
+        head_region = self.clean_trait_region(mask['head'])
+
+        return head_region.area 
+
     def measure_eye_head_ratio(self):
         '''
         Create eye head area ratio
         1- Area head after cleaning  and filling hole
         2- Area eye after cleaning and filing hole
         3- ratio
         '''
-        mask = self.mask
-        head_region = self.clean_trait_region(mask['head'])
-        eye_region = self.clean_trait_region(mask['eye'])
+        eye_areaa = measure_eye_area()
+        head_area = measure_head_area()
 
-        eye_head_ratio = eye_region.area/head_region.area
+        eye_head_ratio = eye_area/head_area
 
-        return eye_head_ratio
+        return eye_head_ratio    
+
 
     def measure_eye_diameter(self):        
         '''
@@ -392,7 +412,7 @@ def measure_eye_diameter(self):
         mask = self.mask
         eye_region = self.clean_trait_region(mask['eye'])
 
-        eq_diameter = (eye_region.area/math.pi)**0.5
+        eq_diameter = eye_region.equivalent_diameter_area
 
         return eq_diameter    
 
@@ -407,6 +427,19 @@ def measure_head_length(self):
 
         return head_length
 
+    def calculate_triangle_area(self, point_1, point_2, point_3):
+
+        # calculate the semi-perimeter
+        a = self.get_distance(point_1, point_2)
+        b = self.get_distance(point_2, point_3)
+        c = self.get_distance(point_3, point_1)
+
+        s = (a + b + c) / 2
+
+        # calculate the area
+        area = (s*(s-a)*(s-b)*(s-c)) ** 0.5
+        return area
+
     def measure_head_depth(self):
         '''
         Measure vertical length of the head passing by the center of the eye
@@ -433,16 +466,15 @@ def all_measure(self):
         landmark = self.landmark
         measure={}
         # Standard length body length 
-        measure['A'] = self.get_distance(landmark['1'],landmark['6'])
-        measure['B'] = self.measure_eye_head_ratio()
-        measure['C'] = self.measure_eye_diameter()
-        measure['D'] = self.measure_head_depth()
-        # Head length landmark 2 to 15
-        measure['E'] = self.get_distance(landmark['2'],landmark['15'])
-        # 
-        measure['F'] = self.get_distance(landmark['1'],landmark['14'])
-        # 
-        measure['G'] = self.get_distance(landmark['1'],landmark['6'])
+        measure['SL'] = self.get_distance(landmark['1'],landmark['6'])
+        measure['EA'] = int(self.measure_eye_area())
+        measure['HAt'] = self.calculate_triangle_area(landmark['1'],landmark['2'],landmark['13'])
+        measure['HAp'] = self.measure_head_area()
+        measure['HCL'] = "WIP"
+        measure['ED'] = self.measure_eye_diameter()
+        measure['HL'] = self.get_distance(landmark['1'],landmark['12'])
+        measure['HD'] = self.get_distance(landmark['2'],landmark['13'])
+        measure['pOD'] = self.get_distance(landmark['1'],landmark['14'])
         return measure
 
     def visualize_landmark(self):

Test_Data/INHS_FISH_18609_landmark.json

@@ -0,0 +1 @@
+{"1": [98, 38], "2": [51, 147], "3": [38, 326], "4": [71, 439], "5": [107, 599], "6": [138, 621], "7": [162, 586], "8": [163, 498], "9": [176, 416], "10": [172, 319], "11": [122, 188], "12": [100, 189], "13": [139, 120], "14": [80, 77], "15": [80, 120]}

Test_Data/INHS_FISH_18609_measure.json

@@ -0,0 +1 @@
+{"SL": 584.3706015877253, "EA": 1134, "HAt": 4161.499999999999, "HAp": 9397, "HCL": "WIP", "ED": 37.99807421080277, "HL": 151.0132444522665, "HD": 92.04890004774636, "pOD": 42.95346318982906, "scale": 390.423, "unit": "cm"}

Test_Data/INHS_FISH_18609_presence.json

@@ -0,0 +1 @@
+{"dorsal_fin": {"number": 1, "percentage": 1.0}, "adipos_fin": {"number": 0, "percentage": 0}, "caudal_fin": {"number": 1, "percentage": 1.0}, "anal_fin": {"number": 1, "percentage": 1.0}, "pelvic_fin": {"number": 2, "percentage": 0.9156065777226187}, "pectoral_fin": {"number": 1, "percentage": 1.0}, "head": {"number": 1, "percentage": 1.0}, "eye": {"number": 1, "percentage": 1.0}, "caudal_fin_ray": {"number": 0, "percentage": 0}, "alt_fin_ray": {"number": 0, "percentage": 0}, "trunk": {"number": 2, "percentage": 0.9499680160833409}}