sim vision

simulate.sh

@@ -23,6 +23,7 @@ python -B -u "./src/main.py" \
 --sim-modbus \
 --sim-os-label-printer \
 --sim-serial \
+--sim-vision \
 --style windows \
 $* 2> >(sed $'s/.*/\e[31m&\e[m/' >&2) # &
 # --about \

src/components/vision.py

@@ -282,6 +282,9 @@ class Vision(Component):
         tf_mode = None
         # reset tflite variables
         interpreter = None
+        if self.simulate:
+            tf_mode = "simulation"
+            interpreter = None
         if tf_mode is None and "edgetpu" in self.allowed_modes:
             try:
                 # create tflite edgetpu interpreter
@@ -323,14 +326,6 @@ class Vision(Component):
         self.loading_model_signal.emit({"status": "done"})
 
     def check_features(self, frame, lock=True):
-        # test_box = [i * s for i, s in zip([0.2, 0.2, 0.8, 0.8], frame.shape[:2] * 2)]
-        # return [{
-        #     "class": self.category_index[1],
-        #     "score": 1.0,
-        #     "box": test_box,  # rescale detection to frame size,
-        #     "center": self.get_center(test_box),
-        #     "size": self.get_size(test_box),
-        # }]
         if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]:
             tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0)
         else:
@@ -338,7 +333,15 @@ class Vision(Component):
         # Run inference
         if lock:
             self.lock.lock()
-        if self.tf_mode in {"edgetpu", "tflite"}:
+        if self.simulate or tf_mode == "simulation":
+            detections = {
+                "detection_scores": [[1.0]],
+                "detection_boxes": [[[0.2, 0.2, 0.8, 0.8]]],
+                "detection_classes": [[1]],
+            }
+            if lock:
+                self.lock.unlock()
+        elif self.tf_mode in {"edgetpu", "tflite"}:
             i_d = self.interpreter.get_input_details()
             # print(i_d)
             o_d = self.interpreter.get_output_details()