Added configuration for realtime object detection

connervieira · Oct 22, 2023 · a55e265 · a55e265
1 parent c917f16
commit a55e265
Show file tree

Hide file tree

Showing 3 changed files with 19 additions and 13 deletions.
diff --git a/TODO.md b/TODO.md
@@ -66,6 +66,7 @@ These are the features actively planned for Predator and are likely to be added
     - [X] Test updated license plate validation in pre-recorded mode.
     - [X] Test updated license plate validation in real-time mode.
 - [X] Add Phantom alert handling to updated ALPR stream.
+    - [ ] Display prominent alerts of the ALPR process fails.
 - [ ] Complete OpenCV dashcam recording.
 - [X] Improve the efficiency of GPS location requests when many requests are made in quick succession.
     - [ ] Test that improved GPS location querying behaves as expected.

diff --git a/config.json b/config.json
@@ -27,7 +27,7 @@
             "debugging_output": false
         },
         "object_recognition": {
-            "enabled": false
+            "enabled": true
         },
         "modes": {
             "auto_start": "",
@@ -75,6 +75,10 @@
                 "clearing": true
             }
         },
+        "object_recognition": {
+            "enabled": true,
+            "video_still_path": "/dev/shm/phantom-webcam.jpg"
+        },
         "gps": {
             "enabled": false,
             "alpr_location_tagging": true

diff --git a/main.py b/main.py
@@ -1318,18 +1318,19 @@
             if (config["realtime"]["interface"]["display"]["output_level"] >= 3): # Only display this status message if the output level indicates to do so.
                 print("Running object recognition...")
 
-            image = cv2.imread(config["realtime"]["object_recognition"]["enabled"]) # Load the frame.
-            object_recognition_bounding_box, object_recognition_labels, object_recognition_confidence = cv.detect_common_objects(image) # Anaylze the image.
-            objects_identified = str(object_recognition_labels) # Convert the list of objects identified into a plain string.
-            if (objects_identified != "[]"): # Check to see that there were actually identified objects.
-                if (config["realtime"]["interface"]["display"]["output_level"] >= 2): # Only display this status message if the output level indicates to do so.
-                    print("Objects identified: " + objects_identified)
-                export_data = str(round(time.time()*10)/10) + "," + objects_identified + "\n" # Add the timestamp to the export data, followed by the object's detected, followed by a line break to prepare for the next entry to be added later.
-                if (save_real_time_object_recognition == True): # Check to make sure the user has configured Predator to save recognized objects to disk.
-                    add_to_file(root + "/real_time_object_detection.csv", export_data, silence_file_saving) # Add the export data to the end of the file and write it to disk.
-
-            if (config["realtime"]["interface"]["display"]["output_level"] >= 3): # Only display this status message if the output level indicates to do so.
-                print("Done\n----------")
+            if (os.path.exists(video_still_path) == True):
+                image = cv2.imread(config["realtime"]["object_recognition"]["video_still_path"]) # Load the frame.
+                object_recognition_bounding_box, object_recognition_labels, object_recognition_confidence = cv.detect_common_objects(image) # Anaylze the image.
+                objects_identified = str(object_recognition_labels) # Convert the list of objects identified into a plain string.
+                if (objects_identified != "[]"): # Check to see that there were actually identified objects.
+                    if (config["realtime"]["interface"]["display"]["output_level"] >= 2): # Only display this status message if the output level indicates to do so.
+                        print("Objects identified: " + objects_identified)
+                    export_data = str(round(time.time()*10)/10) + "," + objects_identified + "\n" # Add the timestamp to the export data, followed by the object's detected, followed by a line break to prepare for the next entry to be added later.
+                    if (save_real_time_object_recognition == True): # Check to make sure the user has configured Predator to save recognized objects to disk.
+                        add_to_file(root + "/real_time_object_detection.csv", export_data, silence_file_saving) # Add the export data to the end of the file and write it to disk.
+
+                if (config["realtime"]["interface"]["display"]["output_level"] >= 3): # Only display this status message if the output level indicates to do so.
+                    print("Done\n----------")