fix error when extracting exif metadata features from images in RAI V…

…ision dashboard

responsibleai_vision/requirements-dev.txt

@@ -28,4 +28,5 @@ opencv-python
 
 fastai
 mlflow
-pydantic<2.0.0
+pydantic<2.0.0
+piexif

responsibleai_vision/responsibleai_vision/common/constants.py

@@ -4,6 +4,12 @@
 from enum import Enum
 
 
+class ExtractedFeatures(str, Enum):
+    """Provide constants related to the extracted image features."""
+
+    MEAN_PIXEL_VALUE = 'mean_pixel_value'
+
+
 class ModelTask(str, Enum):
     """Provide model task constants.
 
@@ -28,6 +34,12 @@ class ImageColumns(str, Enum):
     IMAGE_DETAILS = 'image_details'
 
 
+class ImageModes(str, Enum):
+    """Provide constants related to the image modes."""
+
+    RGB = 'RGB'
+
+
 class ExplainabilityLiterals:
     """Parameters for explainability method names."""
 

responsibleai_vision/responsibleai_vision/rai_vision_insights/rai_vision_insights.py

@@ -39,7 +39,8 @@
                                                    ExplainabilityDefaults,
                                                    ImageColumns,
                                                    MLFlowSchemaLiterals,
-                                                   ModelTask)
+                                                   ModelTask,
+                                                   ImageModes)
 from responsibleai_vision.managers.error_analysis_manager import \
     ErrorAnalysisManager
 from responsibleai_vision.managers.explainer_manager import ExplainerManager
@@ -135,7 +136,7 @@ def __init__(self, model: Any,
                  classes: Optional[np.ndarray] = None,
                  serializer: Optional[Any] = None,
                  maximum_rows_for_test: int = 5000,
-                 image_mode: str = "RGB",
+                 image_mode: str = ImageModes.RGB,
                  test_data_path: Optional[str] = None,
                  transformations: Optional[Any] = None,
                  image_downloader: Optional[Any] = None,
@@ -267,7 +268,7 @@ def __init__(self, model: Any,
             serializer)
 
         ext_test, ext_features = extract_features(
-            self.test, self.target_column, self.task_type,
+            self.test, self.target_column,
             self.image_mode,
             self._feature_metadata)
         self._ext_test = ext_test

responsibleai_vision/responsibleai_vision/utils/feature_extractors.py

@@ -9,15 +9,21 @@
 from PIL import Image
 from PIL.ExifTags import TAGS
 from tqdm import tqdm
+import warnings
 
 from responsibleai.feature_metadata import FeatureMetadata
 from responsibleai_vision.utils.image_reader import (
     get_all_exif_feature_names, get_image_from_path,
     get_image_pointer_from_path)
+from responsibleai_vision.common.constants import ExtractedFeatures
+
+
+MEAN_PIXEL_VALUE = ExtractedFeatures.MEAN_PIXEL_VALUE.value
+MAX_CUSTOM_LEN = 100
 
 
 def extract_features(image_dataset: pd.DataFrame,
-                     target_column: str, task_type: str,
+                     target_column: str,
                      image_mode: str = None,
                      feature_metadata: Optional[FeatureMetadata] = None):
     '''Extract tabular data features from the image dataset.
@@ -27,8 +33,6 @@ def extract_features(image_dataset: pd.DataFrame,
     :param target_column: The name of the label column or list of columns.
         This is a list of columns for multilabel models.
     :type target_column: str or list[str]
-    :param task_type: The type of task to be performed.
-    :type task_type: str
     :param image_mode: The mode to open the image in.
         See pillow documentation for all modes:
         https://pillow.readthedocs.io/en/stable/handbook/concepts.html
@@ -45,7 +49,7 @@ def extract_features(image_dataset: pd.DataFrame,
     if feature_metadata and feature_metadata.categorical_features is None:
         feature_metadata.categorical_features = []
     exif_feature_names = get_all_exif_feature_names(image_dataset)
-    feature_names = ["mean_pixel_value"] + exif_feature_names
+    feature_names = [MEAN_PIXEL_VALUE] + exif_feature_names
 
     # append all feature names other than target column and label
     column_names = image_dataset.columns
@@ -58,6 +62,7 @@ def extract_features(image_dataset: pd.DataFrame,
             continue
         feature_names.append(column_names[j])
 
+    blacklisted_tags = {}
     # append all features
     for i in tqdm(range(image_dataset.shape[0])):
         image = image_dataset.iloc[i, 0]
@@ -81,9 +86,26 @@ def extract_features(image_dataset: pd.DataFrame,
                     # decode bytes
                     if isinstance(data, bytes):
                         data = data.decode()
+                        if len(data) > MAX_CUSTOM_LEN:
+                            data = data[:MAX_CUSTOM_LEN] + '...'
                     if isinstance(data, str):
-                        feature_metadata.categorical_features.append(str(tag))
-                        row_feature_values[feature_names.index(tag)] = data
+                        if not feature_metadata:
+                            feature_metadata = FeatureMetadata()
+                            feature_metadata.categorical_features = []
+                        if tag in feature_names:
+                            feature_metadata.categorical_features.append(
+                                str(tag))
+                            tag_index = feature_names.index(tag)
+                            row_feature_values[tag_index] = data
+                        else:
+                            # in theory this should now never happen with
+                            # latest code, but adding this check for safety
+                            if tag not in blacklisted_tags:
+                                blacklisted_tags.add(tag)
+                                warnings.warn(
+                                    f'Exif tag {tag} could not be found '
+                                    'in the feature names. Ignoring tag '
+                                    'from extracted metadata.')
                     elif isinstance(data, int) or isinstance(data, float):
                         row_feature_values[feature_names.index(tag)] = data
 

responsibleai_vision/responsibleai_vision/utils/image_reader.py

@@ -94,7 +94,8 @@ def get_all_exif_feature_names(image_dataset):
                         data = exifdata.get(tag_id)
                         if isinstance(data, str) or \
                            isinstance(data, int) or \
-                           isinstance(data, float):
+                           isinstance(data, float) or \
+                           isinstance(data, bytes):
                             exif_feature_names.add(tag)
     return list(exif_feature_names)
 

responsibleai_vision/tests/common_vision_utils.py

@@ -12,6 +12,7 @@
 
 import numpy as np
 import pandas as pd
+import piexif
 import shap
 import torch
 import torch.nn as nn
@@ -172,7 +173,7 @@ def retrieve_unzip_file(download_url, data_file):
     os.remove(data_file)
 
 
-def load_fridge_dataset():
+def load_fridge_dataset(add_extra_mixed_metadata=False):
     # create data folder if it doesnt exist.
     os.makedirs("data", exist_ok=True)
 
@@ -186,6 +187,14 @@ def load_fridge_dataset():
     for folder in os.listdir("./data/fridgeObjects"):
         for file in os.listdir("./data/fridgeObjects/" + folder):
             image_path = "./data/fridgeObjects/" + folder + "/" + file
+            if add_extra_mixed_metadata and file.endswith("1.jpg"):
+                with Image.open(image_path) as im:
+                    exif_dict = piexif.load(im.info['exif'])
+                    comment = 'Extra metadata for {}'.format(file).encode()
+                    exif_dict['0th'][piexif.ImageIFD.XPComment] = comment
+                    exif_dict['1st'][piexif.ImageIFD.XPComment] = comment
+                    exif_bytes = piexif.dump(exif_dict)
+                    im.save(image_path, exif=exif_bytes)
             data = data.append({IMAGE: image_path, LABEL: folder},
                                ignore_index=True)
     return data

responsibleai_vision/tests/test_feature_extractors.py

@@ -0,0 +1,71 @@
+# Copyright (c) Microsoft Corporation
+# Licensed under the MIT License.
+
+import numpy as np
+from common_vision_utils import (load_fridge_object_detection_dataset, load_flowers_dataset, load_imagenet_dataset, load_fridge_dataset)
+from responsibleai_vision import ModelTask
+from responsibleai_vision.common.constants import (ExplainabilityDefaults,
+                                                   ExtractedFeatures,
+                                                   ImageColumns,
+                                                   ImageModes)
+from responsibleai_vision.utils.feature_extractors import extract_features
+
+
+MEAN_PIXEL_VALUE = ExtractedFeatures.MEAN_PIXEL_VALUE.value
+FRIDGE_METADATA_FEATURES = [
+    'Make', 'ResolutionUnit', 'ImageLength', 'ExifOffset', 'Model',
+    'GPSInfo', 'ImageWidth', 'DateTime', 'YCbCrPositioning',
+    'Software', 'Orientation'
+]
+
+
+def validate_extracted_features(extracted_features, feature_names,
+                                expected_feature_names, data):
+        assert len(extracted_features) == len(data)
+        assert feature_names[0] == expected_feature_names[0]
+        for i in range(1, len(feature_names)):
+            assert feature_names[i] in expected_feature_names
+        assert len(feature_names) == len(expected_feature_names)
+        assert len(extracted_features[0]) == len(feature_names)
+
+
+def extract_dataset_features(data):
+    return extract_features(data, ImageColumns.LABEL, ImageModes.RGB, None)
+
+
+class TestFeatureExtractors(object):
+    def test_extract_features_fridge_object_detection(self):
+        data = load_fridge_object_detection_dataset(automl_format=False)
+        extracted_features, feature_names = extract_dataset_features(data)
+        expected_feature_names = [MEAN_PIXEL_VALUE] + FRIDGE_METADATA_FEATURES
+        validate_extracted_features(extracted_features, feature_names,
+                                    expected_feature_names, data)
+
+    def test_extract_features_fridge_metadata(self):
+        data = load_fridge_dataset()
+        extracted_features, feature_names = extract_dataset_features(data)
+        expected_feature_names = [MEAN_PIXEL_VALUE] + FRIDGE_METADATA_FEATURES
+        validate_extracted_features(extracted_features, feature_names,
+                                    expected_feature_names, data)
+
+    def test_extract_features_imagenet_metadata(self):
+        data = load_imagenet_dataset()
+        extracted_features, feature_names = extract_dataset_features(data)
+        expected_feature_names = [MEAN_PIXEL_VALUE]
+        validate_extracted_features(extracted_features, feature_names,
+                                    expected_feature_names, data)
+
+    def test_extract_features_flowers_metadata(self):
+        data = load_flowers_dataset(upscale=False)
+        extracted_features, feature_names = extract_dataset_features(data)
+        expected_feature_names = [MEAN_PIXEL_VALUE]
+        validate_extracted_features(extracted_features, feature_names,
+                                    expected_feature_names, data)
+
+    def test_extract_features_mixed_exif_XPComment_metadata(self):
+        data = load_fridge_dataset(add_extra_mixed_metadata=True)
+        extracted_features, feature_names = extract_dataset_features(data)
+        expected_feature_names = [MEAN_PIXEL_VALUE, 'XPComment']
+        expected_feature_names += FRIDGE_METADATA_FEATURES
+        validate_extracted_features(extracted_features, feature_names,
+                                    expected_feature_names, data)

responsibleai_vision/tests/test_rai_vision_insights.py

@@ -25,7 +25,8 @@
 from responsibleai.feature_metadata import FeatureMetadata
 from responsibleai_vision import ModelTask, RAIVisionInsights
 from responsibleai_vision.common.constants import (ExplainabilityDefaults,
-                                                   ImageColumns)
+                                                   ImageColumns,
+                                                   ImageModes)
 
 DEFAULT_MAX_EVALS = ExplainabilityDefaults.DEFAULT_MAX_EVALS
 DEFAULT_NUM_MASKS = ExplainabilityDefaults.DEFAULT_NUM_MASKS
@@ -40,7 +41,7 @@ def test_rai_insights_image_classification_imagenet(self):
         task_type = ModelTask.IMAGE_CLASSIFICATION
         class_names = load_imagenet_labels()
         run_rai_insights(pred, data[:3], ImageColumns.LABEL,
-                         task_type, class_names, image_mode='RGB')
+                         task_type, class_names, image_mode=ImageModes.RGB)
 
     @pytest.mark.parametrize('max_evals', [None, 10, 200])
     def test_rai_insights_image_classification_max_evals(self, max_evals):
@@ -51,7 +52,7 @@ def test_rai_insights_image_classification_max_evals(self, max_evals):
         # run on a single image to avoid running out of memory on
         # test machines
         run_rai_insights(pred, data[:1], ImageColumns.LABEL,
-                         task_type, class_names, image_mode='RGB',
+                         task_type, class_names, image_mode=ImageModes.RGB,
                          test_explainer=True, max_evals=max_evals)
 
     @pytest.mark.parametrize('max_evals', [-100, -1, 0])
@@ -63,7 +64,8 @@ def test_rai_insights_invalid_max_evals(self, max_evals):
         with pytest.raises(ValueError,
                            match="max_evals must be greater than 0"):
             run_rai_insights(pred, data[:1], ImageColumns.LABEL,
-                             task_type, class_names, image_mode='RGB',
+                             task_type, class_names,
+                             image_mode=ImageModes.RGB,
                              test_explainer=True, max_evals=max_evals)
 
     def test_rai_insights_image_classification_fridge(self):