merge develop

Author: previ

.circleci/config.yml

@@ -7,7 +7,7 @@ jobs:
   build:
     docker:
       # specify the version you desire here
-      - image: coderbot/python-gpac:3.5
+      - image: coderbot/python-gpac:3.5-tf2
 
     working_directory: ~/repo
 
@@ -26,6 +26,7 @@ jobs:
           command: |
             python3 -m venv venv
             . venv/bin/activate
+            pip install --upgrade pip
             pip install -r requirements_stub.txt
 
       - save_cache:
@@ -41,8 +42,8 @@ jobs:
             export PYTHONPATH=./stub:test
             mkdir test-reports
             python3 -m unittest test/coderbot_test.py 2>&1 | tee test-reports/test_report.txt
-            #python3 -m unittest test/cnn_test.py 2>&1 | tee test-reports/test_report.txt
             python3 -m unittest test/camera_test.py 2>&1 | tee test-reports/test_report.txt
+            #python3 -m unittest test/cnn_test.py 2>&1 | tee test-reports/test_report.txt
             echo "test complete"
       - store_artifacts:
           path: test-reports/

.gitignore

@@ -37,6 +37,7 @@ photos/*.jpg
 photos/*.mp4
 photos/*.h264
 photos/*.json
+saved_photos
 
 #cnn models
 cnn_models/*

MPU6050.py

@@ -358,15 +358,39 @@ def cnn_classify(self, model_name=None, top_results=3):
         else:
             classifier = self._cnn_classifier_default
 
+        t0 = time.time()
         classes = None
         try:
             img = self.get_image()
             classes = classifier.classify_image(img.mat(), top_results=top_results)
         except Exception:
             logging.warning("classifier not available")
             classes = [("None", 1.0)]
+            raise
+        logging.info("fps: %f", 1.0/(time.time()-t0))
         return classes
 
     def find_class(self):
         return self.cnn_classify(top_results=1)[0][0]
 
+    def cnn_detect_objects(self, model_name=None, top_results=3):
+        classifier = None
+        if model_name:
+            classifier = self._cnn_classifiers.get(model_name)
+            if classifier is None:
+                classifier = CNNManager.get_instance().load_model(model_name)
+                self._cnn_classifiers[model_name] = classifier
+        else:
+            classifier = self._cnn_classifier_default
+
+        t0 = time.time()
+        classes = None
+        try:
+            img = self.get_image()
+            classes = classifier.detect_objects(img.mat(), top_results=top_results)
+        except Exception:
+            logging.warning("classifier not available")
+            classes = [("None", 100)]
+            raise
+        logging.info("fps: %f", 1.0/(time.time()-t0))
+        return classes