Merge pull request #35 from thibaulttabarin/main

Correct the alignment of the fish

Scripts/Morphology_main.py

@@ -67,17 +67,18 @@ def main(input_file, metadata_file, output_measure, output_landmark, output_pres
          output_lm_image=None):
 
     # Create the image segmentation object
-    img_seg = tc.segmented_image(input_file)
+    img_seg = tc.Segmented_image(input_file, align = True)
     base_name = img_seg.base_name
+    # Create object measure_morphology
+    measure_morph = tc.Measure_morphology(input_file, align = True)
     # 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
+    measurements_bbox = measure_morph.measurement_with_bbox
+    measurements_lm = measure_morph.measurement_with_lm
+    measurements_area = measure_morph.measurement_with_area
+    landmark = measure_morph.landmark
+    presence_matrix = measure_morph.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 }
@@ -108,7 +109,9 @@ def main(input_file, metadata_file, output_measure, output_landmark, output_pres
 
     if output_lm_image:
 
-        img_landmark = img_seg.visualize_landmark()
+
+        # create landmark visualization image and save it
+        img_landmark = measure_morph.visualize_landmark()
         img_landmark.save(output_lm_image)
 
 

Scripts/Traits_class.py

@@ -15,33 +15,31 @@
 from skimage.morphology import reconstruction
 
 
-class segmented_image:
+class Segmented_image:
 
-    def __init__(self, file_name):
+    def __init__(self, file_name, align = True, cutoff = 0.6):
         self.file = file_name
         self.image_name = os.path.split(file_name)[1]
         self.base_name = self.image_name.rsplit('_',1)[0]
+        self.cutoff = cutoff
+        self.align = align
+
         self.trait_color_dict={'background': [0, 0, 0],'dorsal_fin': [254, 0, 0],'adipos_fin': [0, 254, 0],
                                'caudal_fin': [0, 0, 254],'anal_fin': [254, 254, 0],'pelvic_fin': [0, 254, 254],
                                'pectoral_fin': [254, 0, 254],'head': [254, 254, 254],'eye': [0, 254, 102],
                                'caudal_fin_ray': [254, 102, 102],'alt_fin_ray': [254, 102, 204],
                                'trunk': [0, 124, 124]}
-        self.cutoff = 0.60
+
         self.img_arr = self.import_image(file_name)
+        self.fish_angle = self.get_fish_angle_pca()
+        if align:
+            self.img_arr = self.align_fish()
+            self.old_fish_angle = self.fish_angle
+            self.fish_angle = self.get_fish_angle_pca()
+
         self.get_channels_mask()
         self.presence_matrix = self.get_presence_matrix()
-        self.fish_angle = self.get_fish_angle_pca()
-
-    def get_all_measures_landmarks(self):
-        '''
-        Execute the multiple functions that calculate landmarks and measurements
-        '''
-        self.landmark = self.all_landmark()
-        self.measurement_with_bbox = self.all_measure_using_bbox()
-        self.measurement_with_lm = self.all_measure_using_lm()
-        self.measurement_with_area = self.all_measure_area()
-
-
+
     def import_image(self,file_name):
         '''
         Import the image from "image_path" and convert to np.array astype uint8 (0-255)
@@ -50,7 +48,43 @@ def import_image(self,file_name):
         img_arr = np.array(img, dtype=np.uint8)
 
         return img_arr
-
+
+    def get_fish_angle_pca(self):
+        '''
+        Calculate orientation (PCA) of the mask of whole fish
+        We choose to combine whole fish part and calculate orientation.
+        return value in degree
+        '''
+
+        # create a mask with all the fish traits 
+        img_arr = self.img_arr
+        whole_fish = np.sum(img_arr,axis=2).astype(bool)      
+
+        # Clean holes and remove isolated blobs and create a regionprop
+        trait_region = self.clean_trait_region(whole_fish)   
+        angle_rad = trait_region.orientation
+        #fish_angle = (90-angle_rad*180/math.pi)
+        fish_angle = np.sign(angle_rad) * (90-abs(angle_rad*180/math.pi))
+
+        # + 0.0 remove negative sign on rounded 0.0 value
+        return round(fish_angle,2) + 0.0 
+
+
+    def align_fish(self):
+        '''
+        Development
+        To align the fish horizontally
+        in order to get landmark 5 and 6
+        '''
+
+        img_arr = self.img_arr
+        angle_deg = self.fish_angle
+
+        image_align = Image.fromarray(img_arr).rotate(angle_deg)
+
+        return  np.array(image_align, dtype=np.uint8)    
+
+
     def get_channels_mask(self):
         ''' 
         Convert the png image (numpy.ndarray, np.uint8)  (320, 800, 3)
@@ -75,41 +109,6 @@ def get_channels_mask(self):
 
         self.mask = mask
 
-    def get_fish_angle_pca(self):
-        '''
-        Calculate orientation (PCA) of the mask of whole fish
-        We choose to combine whole fish part and calculate orientation.
-        return value in degree
-        '''
-        fish_angle = "None"
-        mask = self.mask
-        whole_fish = np.zeros_like(None,dtype="uint8")
-        for i,(k,v) in enumerate(mask.items()):
-                whole_fish = whole_fish +v
-
-        # Check that the mask is not empty
-        if np.any(whole_fish):
-
-            trait_region = self.clean_trait_region(whole_fish)
-            angle_rad = trait_region.orientation
-            fish_angle = (90-angle_rad*180/math.pi)
-
-        return round(fish_angle,2)
-
-    def align_fish(self):
-        '''
-        Development
-        To align the fish horizontally
-        in order to get landmark 5 and 6
-        '''
-
-        img_arr = self.img_arr
-        angle_deg = self.fish_angle
-
-        image_align = Image.fromarray(img_arr).rotate(angle_deg)
-
-        return image_align
-
     def remove_holes(self, image):
 
         seed = np.copy(image)
@@ -171,27 +170,23 @@ def get_presence_matrix(self):
             presence_matrix[trait_name] = temp_dict
 
         return presence_matrix
+
+
+class Measure_morphology(Segmented_image):
 
+    def __init__(self, file_name, align=True):
+
+        super().__init__(file_name, align=True)
+        self.get_all_measures_landmarks()
 
-    def get_one_property_all_trait(self, property_='centroid'):
+    def get_all_measures_landmarks(self):
         '''
-        Create a dictionnary with key = trait and value the property selected by property_
-        example: {'dorsal_fin': (centroid[0], centroid[1]), 'trunk':(centroid[0], centroid[1])....}
+        Execute the multiple functions that calculate landmarks and measurements
         '''
-
-        mask = self.mask
-        dict_property={}
-        # enumerate through the dictionary of mask to collect trait_name
-        for i, (trait_name, trait_mask) in enumerate(mask.items()):
-
-            trait_region = self.clean_trait_region(trait_mask)
-
-            if trait_region:  
-                dict_property[trait_name] = trait_region[property_]
-            else:
-                dict_property[trait_name]=None
-
-        return dict_property
+        self.landmark = self.all_landmark()
+        self.measurement_with_bbox = self.all_measure_using_bbox()
+        self.measurement_with_lm = self.all_measure_using_lm()
+        self.measurement_with_area = self.all_measure_area()
 
     def get_distance(self, a,b):
         '''
@@ -565,11 +560,7 @@ def all_measure_area(self):
         measure_area['HA_m'] = self.measure_head_area()
 
         return measure_area
-
-
-
-
-
+
 ########################
 # Measurement using bbox
 ########################
@@ -628,7 +619,7 @@ def all_measure_using_bbox(self):
         '''
         Collect the measurment for the fish for Meghan paper
         '''
-        measures_bbox={'SL_bbox':'None', 'HL_bbox':'None', 'ED_bbox':'None','pOD_bbox':'None', 'fish_angle':'None' }
+        measures_bbox={'SL_bbox':'None', 'HL_bbox':'None', 'ED_bbox':'None','pOD_bbox':'None', 'FA_pca':'None' }
 
         # SL standart length, length bbox of head+trunk
 
@@ -647,9 +638,36 @@ def all_measure_using_bbox(self):
 
         return measures_bbox
 
+    def visualize_landmark(self):
+
+        landmark = self.all_landmark()
+        img_arr = self.img_arr
+        img = Image.fromarray(img_arr)
+        img1 = ImageDraw.Draw(img)
+
+        #
+        #fnt = ImageFont.truetype("Pillow/Tests/fonts/FreeMono.ttf", 15)
+        fnt = ImageFont.load_default()
+        for i,(k,v) in enumerate(landmark.items()):
+
+            # landmark exist draw it on the image
+            if v:
+                row,col = v
+                xy = [(col-9,row-9),(col+9,row+9)]
+                img1.ellipse(xy, fill='gray', outline=None, width=1)
+
+                img1.text((col-6, row-6), k, font=fnt, fill='black')
+            # Display the image created
+        return img    
+
+class Visualization_morphology(Measure_morphology):     
 ############################
 # Visualization function
 ############################
+    def __init__(self, file_name, align=True):
+
+        super().__init__(file_name, align=True)
+
     def visualize_trait(self, trait):
 
         mask = self.mask