Re-align models and views to the unified ones.

Author: jonathanrocher

stage8_packaging/pycasa/model/image_file.py

@@ -1,16 +1,17 @@
 # General imports
-import os
+from os.path import splitext
 import PIL.Image
 from PIL.ExifTags import TAGS
 from skimage import data
 from skimage.feature import Cascade
 import numpy as np
 
 # ETS imports
-from traits.api import Array, cached_property, Dict, File, HasStrictTraits, \
-    List, Property
+from traits.api import (
+    Array, cached_property, Dict, File, HasStrictTraits, List, Property
+)
 
-SUPPORTED_FORMATS = [".png", ".jpg", ".jpeg"]
+SUPPORTED_FORMATS = [".png", ".jpg", ".jpeg", ".PNG", ".JPG", ".JPEG"]
 
 
 class ImageFile(HasStrictTraits):
@@ -24,43 +25,40 @@ class ImageFile(HasStrictTraits):
 
     faces = List
 
-    def to_array(self):
-        file_ext = os.path.splitext(self.filepath)[1].lower()
-        if not self.filepath or file_ext not in SUPPORTED_FORMATS:
-            return np.array([])
-
-        with PIL.Image.open(self.filepath) as img:
-            return np.asarray(img)
+    def _is_valid_file(self):
+        return (
+            bool(self.filepath) and
+            splitext(self.filepath)[1].lower() in SUPPORTED_FORMATS
+        )
 
     @cached_property
     def _get_data(self):
-        return self.to_array()
+        if not self._is_valid_file():
+            return np.array([])
+        with PIL.Image.open(self.filepath) as img:
+            return np.asarray(img)
 
     @cached_property
     def _get_metadata(self):
-        file_ext = os.path.splitext(self.filepath)[1].lower()
-        if not self.filepath or file_ext not in SUPPORTED_FORMATS:
+        if not self._is_valid_file():
             return {}
-
         with PIL.Image.open(self.filepath) as img:
             exif = img._getexif()
-
-        if exif:
-            return {TAGS[k]: v for k, v in exif.items()
-                    if k in TAGS}
-        else:
+        if not exif:
             return {}
+        return {TAGS[k]: v for k, v in exif.items() if k in TAGS}
 
-    def detect_faces(self, scale_factor=1.2, step_ratio=1, min_size=60,
-                     max_size=600):
-        """ Detect faces in the image.
-        """
+    def detect_faces(self):
+        # Load the trained file from the module root.
         trained_file = data.lbp_frontal_face_cascade_filename()
+
+        # Initialize the detector cascade.
         detector = Cascade(trained_file)
-        faces = detector.detect_multi_scale(img=self.data,
-                                            scale_factor=scale_factor,
-                                            step_ratio=step_ratio,
-                                            min_size=(min_size, min_size),
-                                            max_size=(max_size, max_size))
-        self.faces = faces
-        return faces
+
+        detected = detector.detect_multi_scale(img=self.data,
+                                               scale_factor=1.2,
+                                               step_ratio=1,
+                                               min_size=(60, 60),
+                                               max_size=(600, 600))
+        self.faces = detected
+        return self.faces

stage8_packaging/pycasa/model/image_folder.py

@@ -1,24 +1,28 @@
 # General imports
-import os
+import glob
+from os.path import basename, expanduser, isdir
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 # ETS imports
-from traits.api import Directory, Event, HasStrictTraits, Instance
+from traits.api import (
+    Directory, Event, HasStrictTraits, Instance, List, observe,
+)
 
 # Local imports
-from .image_file import ImageFile, SUPPORTED_FORMATS
+from pycasa.model.image_file import ImageFile, SUPPORTED_FORMATS
 
 FILENAME_COL = "filename"
-
 NUM_FACE_COL = "Num. faces"
 
 
 class ImageFolder(HasStrictTraits):
-    """ Model to hold an image folder.
+    """ Model for a folder of images.
     """
-    path = Directory
+    directory = Directory(expanduser("~"))
+
+    images = List(Instance(ImageFile))
 
     data = Instance(pd.DataFrame)
 
@@ -27,39 +31,39 @@ class ImageFolder(HasStrictTraits):
     def __init__(self, **traits):
         # Don't forget this!
         super(ImageFolder, self).__init__(**traits)
-        if not os.path.isdir(self.path):
-            msg = f"Unable to create an ImageFolder from {self.path} since" \
-                  f" it is not a valid directory."
+        if not isdir(self.directory):
+            msg = f"The provided directory isn't a real directory: " \
+                  f"{self.directory}"
             raise ValueError(msg)
+        self.data = self._create_metadata_df()
 
-        self.data = self.to_dataframe()
+    @observe("directory")
+    def _update_images(self, event):
+        self.images = [
+            ImageFile(filepath=file)
+            for fmt in SUPPORTED_FORMATS
+            for file in glob.glob(f"{self.directory}/*{fmt}")
+        ]
 
-    def to_dataframe(self):
-        if not self.path:
-            return pd.DataFrame({FILENAME_COL: [], NUM_FACE_COL: []})
+    @observe("images.items")
+    def _update_metadata(self, event):
+        self.data = self._create_metadata_df()
 
-        data = []
-        for filename in os.listdir(self.path):
-            file_ext = os.path.splitext(filename)[1].lower()
-            if file_ext in SUPPORTED_FORMATS:
-                filepath = os.path.join(self.path, filename)
-                img_file = ImageFile(filepath=filepath)
-                file_data = {FILENAME_COL: filename, NUM_FACE_COL: np.nan}
-                try:
-                    file_data.update(img_file.metadata)
-                except Exception:
-                    pass
-                data.append(file_data)
+    def _create_metadata_df(self):
+        if not self.images:
+            return pd.DataFrame({FILENAME_COL: [], NUM_FACE_COL: []})
+        return pd.DataFrame([
+                {
+                    FILENAME_COL: basename(img.filepath),
+                    NUM_FACE_COL: np.nan,
+                    **img.metadata
 
-        return pd.DataFrame(data)
+                }
+                for img in self.images
+        ])
 
     def compute_num_faces(self, **kwargs):
-        cols = list(self.data.columns)
-        for i, filename in enumerate(self.data[FILENAME_COL]):
-            print(filename)
-            filepath = os.path.join(self.path, filename)
-            img_file = ImageFile(filepath=filepath)
-            faces = img_file.detect_faces(**kwargs)
-            j = cols.index(NUM_FACE_COL)
-            self.data.iloc[i, j] = len(faces)
+        for i, image in enumerate(self.images):
+            faces = image.detect_faces(**kwargs)
+            self.data[NUM_FACE_COL].iat[i] = len(faces)
             self.data_updated = True

stage8_packaging/pycasa/ui/image_file_view.py

@@ -34,17 +34,18 @@ class ImageFileView(ModelView):
     def build_mpl_figure(self, event):
         figure = Figure()
         axes = figure.add_subplot(111)
-        axes.imshow(self.model.to_array())
+        axes.imshow(self.model.data)
         self.figure = figure
 
-    def _detect_button_fired(self):
+    @observe("detect_button")
+    def _detect_button_fired(self, event):
         self.model.detect_faces()
 
     @observe("model.faces")
     def update_mpl_figure_with_faces(self, events):
         figure = Figure()
         axes = figure.add_subplot(111)
-        axes.imshow(self.model.to_array())
+        axes.imshow(self.model.data)
 
         for face in self.model.faces:
             axes.add_patch(

stage8_packaging/pycasa/ui/image_folder_editor.py

@@ -30,7 +30,7 @@ def create(self, parent):
     # -------------------------------------------------------------------------
 
     def _get_name(self):
-        return self.obj.path[:25]
+        return self.obj.directory[:25]
 
     def _get_tooltip(self):
-        return self.obj.path
+        return self.obj.directory

stage8_packaging/pycasa/ui/image_folder_view.py

@@ -9,7 +9,7 @@
 from traitsui.ui_editors.data_frame_editor import DataFrameEditor
 
 # Local imports
-from ..model.image_folder import FILENAME_COL, ImageFolder, NUM_FACE_COL
+from pycasa.model.image_folder import FILENAME_COL, ImageFolder, NUM_FACE_COL
 
 
 DISPLAYED_COLUMNS = [FILENAME_COL, NUM_FACE_COL] + [
@@ -19,6 +19,12 @@
     'ISOSpeedRatings', 'SceneType'
 ]
 
+YEAR_KEY = "__year__"
+
+DATETIME_COL = "DateTime"
+
+MAKE_COL = 'Make'
+
 
 class ImageFolderView(ModelView):
     """ ModelView for an image folder object.
@@ -30,6 +36,10 @@ class ImageFolderView(ModelView):
     # Filters widgets
     view_filter_controls = Bool
 
+    # Copy of the model's data, with filtering columns added if missing
+    all_data = Instance(pd.DataFrame)
+
+    # Filtered dataframe based on filtering widgets
     filtered_data = Instance(pd.DataFrame)
 
     year_mask = Instance(pd.Series)
@@ -44,7 +54,7 @@ class ImageFolderView(ModelView):
 
     def traits_view(self):
         view = View(
-            Item("model.path", style="readonly", show_label=False),
+            Item("model.directory", style="readonly", show_label=False),
             HGroup(
                 Spring(),
                 Item("view_filter_controls"),
@@ -73,7 +83,6 @@ def traits_view(self):
                      enabled_when="len(model.data) > 0"),
                 Spring(),
             ),
-            resizable=True, height=800
         )
         return view
 
@@ -82,22 +91,22 @@ def traits_view(self):
     @observe("scan")
     def scan_for_faces(self, event):
         self.model.compute_num_faces()
+        self.all_data.update(self.model.data)
 
     @observe("selected_years")
     def update_years(self, event):
-        self.year_mask = self.model.data['Year'].isin(self.selected_years)
+        self.year_mask = self.all_data[YEAR_KEY].isin(self.selected_years)
 
     @observe("selected_make")
     def update_make(self, event):
         if self.selected_make == "All":
-            self.make_mask = pd.Series(np.ones(len(self.model.data),
-                                               dtype=bool))
+            self.make_mask = pd.Series([True] * len(self.model.data))
         else:
-            self.make_mask = self.model.data['Make'] == self.selected_make
+            self.make_mask = self.all_data[MAKE_COL] == self.selected_make
 
-    @observe("year_mask, make_mask")
+    @observe("year_mask, make_mask, all_data")
     def update_filtered_data(self, event):
-        self.filtered_data = self.model.data[self.year_mask & self.make_mask]
+        self.filtered_data = self.all_data[self.year_mask & self.make_mask]
 
     # Initialization methods --------------------------------------------------
 
@@ -107,15 +116,27 @@ def _make_mask_default(self):
     def _year_mask_default(self):
         return pd.Series(np.ones(len(self.model.data), dtype=bool))
 
-    def _filtered_data_default(self):
-        return self.model.data
+    def _all_data_default(self):
+        # Enrich metadata with missing fields: date time, make
+        data = self.model.data.copy()
+
+        if DATETIME_COL not in data.columns:
+            data[DATETIME_COL] = np.nan
+
+        if MAKE_COL not in data.columns:
+            data[MAKE_COL] = np.nan
 
-    def _all_years_default(self):
         def parse_year(x):
             return x.split(":")[0] if isinstance(x, str) else "unknown"
+        data[YEAR_KEY] = data[DATETIME_COL].apply(parse_year)
+
+        return data
 
-        self.model.data['Year'] = self.model.data['DateTime'].apply(parse_year)
-        return sorted(self.model.data['Year'].unique().tolist())
+    def _filtered_data_default(self):
+        return self.all_data
+
+    def _all_years_default(self):
+        return sorted(self.all_data[YEAR_KEY].unique().tolist())
 
 
 if __name__ == '__main__':

stage8_packaging/pycasa/ui/tasks/pycasa_task.py

@@ -57,7 +57,7 @@ def open_in_central_pane(self, filepath):
             obj = ImageFile(filepath=filepath)
             self.central_pane.edit(obj, factory=ImageFileEditor)
         elif file_ext == "":
-            obj = ImageFolder(path=filepath)
+            obj = ImageFolder(directory=filepath)
             self.central_pane.edit(obj, factory=ImageFolderEditor)
         else:
             print("Unsupported file format: {}".format(file_ext))