Limit combinations of backends and targets in demos and benchmark (#145)

* limit backend and target combination in demos and benchmark

* simpler version checking

demo.py

@@ -11,33 +11,36 @@ import cv2 as cv
 
 from pphumanseg import PPHumanSeg
 
-def str2bool(v):
-    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
-        return True
-    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
-        return False
-    else:
-        raise NotImplementedError
-
-backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
-targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
-help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
-help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
-try:
-    backends += [cv.dnn.DNN_BACKEND_TIMVX]
-    targets += [cv.dnn.DNN_TARGET_NPU]
-    help_msg_backends += "; {:d}: TIMVX"
-    help_msg_targets += "; {:d}: NPU"
-except:
-    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.')
+# Check OpenCV version
+assert cv.__version__ >= "4.7.0", \
+       "Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
+
+# Valid combinations of backends and targets
+backend_target_pairs = [
+    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
+    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
+    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
+]
 
 parser = argparse.ArgumentParser(description='PPHumanSeg (https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/contrib/PP-HumanSeg)')
-parser.add_argument('--input', '-i', type=str, help='Usage: Set input path to a certain image, omit if using camera.')
-parser.add_argument('--model', '-m', type=str, default='human_segmentation_pphumanseg_2023mar.onnx', help='Usage: Set model path, defaults to human_segmentation_pphumanseg_2023mar.onnx.')
-parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
-parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
-parser.add_argument('--save', '-s', type=str, default=False, help='Usage: Set “True” to save a file with results. Invalid in case of camera input. Default will be set to “False”.')
-parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Usage: Default will be set to “True” and will open a new window to show results. Set to “False” to stop visualizations from being shown. Invalid in case of camera input.')
+parser.add_argument('--input', '-i', type=str,
+                    help='Usage: Set input path to a certain image, omit if using camera.')
+parser.add_argument('--model', '-m', type=str, default='human_segmentation_pphumanseg_2023mar.onnx',
+                    help='Usage: Set model path, defaults to human_segmentation_pphumanseg_2023mar.onnx.')
+parser.add_argument('--backend_target', '-bt', type=int, default=0,
+                    help='''Choose one of the backend-target pair to run this demo:
+                        {:d}: (default) OpenCV implementation + CPU,
+                        {:d}: CUDA + GPU (CUDA),
+                        {:d}: CUDA + GPU (CUDA FP16),
+                        {:d}: TIM-VX + NPU,
+                        {:d}: CANN + NPU
+                    '''.format(*[x for x in range(len(backend_target_pairs))]))
+parser.add_argument('--save', '-s', action='store_true',
+                    help='Usage: Specify to save a file with results. Invalid in case of camera input.')
+parser.add_argument('--vis', '-v', action='store_true',
+                    help='Usage: Specify to open a new window to show results. Invalid in case of camera input.')
 args = parser.parse_args()
 
 def get_color_map_list(num_classes):
@@ -97,8 +100,10 @@ def visualize(image, result, weight=0.6, fps=None):
 
 
 if __name__ == '__main__':
+    backend_id = backend_target_pairs[args.backend_target][0]
+    target_id = backend_target_pairs[args.backend_target][1]
     # Instantiate PPHumanSeg
-    model = PPHumanSeg(modelPath=args.model, backendId=args.backend, targetId=args.target)
+    model = PPHumanSeg(modelPath=args.model, backendId=backend_id, targetId=target_id)
 
     if args.input is not None:
         # Read image and resize to 192x192

pphumanseg.py

@@ -28,12 +28,10 @@ class PPHumanSeg:
     def name(self):
         return self.__class__.__name__
 
-    def setBackend(self, backend_id):
-        self._backendId = backend_id
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
         self._model.setPreferableBackend(self._backendId)
-
-    def setTarget(self, target_id):
-        self._targetId = target_id
         self._model.setPreferableTarget(self._targetId)
 
     def _preprocess(self, image):
@@ -69,4 +67,3 @@ class PPHumanSeg:
 
         result = np.argmax(outputBlob, axis=1).astype(np.uint8)
         return result
-