fix: content categoriser updates

Author: jaydeepsingh25

preprocessors/depth-map-gen/depth-map-generator.py

@@ -103,8 +103,8 @@ def depthgenerator():
     classifier_1 = "ca.mcgill.a11y.image.preprocessor.contentCategoriser"
     if classifier_1 in preprocess_output:
         classifier_1_output = preprocess_output[classifier_1]
-        classifier_1_label = classifier_1_output.get("category", "")
-        if classifier_1_label != "photograph":
+        categoriser_tags = classifier_1_output["categories"]
+        if not categoriser_tags["photo"]:
             logging.info("Not photograph content. Skipping...")
             return "", 204
     else:

preprocessors/graphic-tagger/azure_api.py

@@ -139,9 +139,8 @@ def categorise():
         if content_classifier in preprocess_output:
             content_classifier_output = \
                 preprocess_output[content_classifier]
-            content_label = \
-                content_classifier_output["category"]
-            if content_label == "photograph":
+            categoriser_tags = content_classifier_output["categories"]
+            if categoriser_tags["photo"]:
                 source = content["graphic"]
                 image_b64 = source.split(",")[1]
                 binary = base64.b64decode(image_b64)

preprocessors/mmsemseg/segment.py

@@ -161,8 +161,9 @@ def segment():
     # if the architecture is modified appropriately
     if classifier_1 in preprocess_output:
         classifier_1_output = preprocess_output[classifier_1]
-        classifier_1_label = classifier_1_output["category"]
-        if classifier_1_label != "photograph":
+        categoriser_tags = classifier_1_output["categories"]
+
+        if not categoriser_tags["photo"]:
             logging.info("Not photograph content. Skipping...")
             return "", 204
         if classifier_2 in preprocess_output:

preprocessors/object-detection-azure/objdetect.py

@@ -137,9 +137,9 @@ def categorise():
         if content_classifier in preprocess_output:
             content_classifier_output = \
                 preprocess_output[content_classifier]
-            content_label = \
-                content_classifier_output["category"]
-            if content_label == "photograph":
+            content_tags = \
+                content_classifier_output["categories"]
+            if content_tags["photo"]:
                 source = content["graphic"]
                 image_b64 = source.split(",")[1]
                 binary = base64.b64decode(image_b64)