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

@@ -5,37 +5,44 @@ import cv2 as cv
 
 from lpd_yunet import LPD_YuNet
 
-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='LPD-YuNet for License Plate Detection')
-parser.add_argument('--input', '-i', type=str, help='Usage: Set path to the input image. Omit for using default camera.')
-parser.add_argument('--model', '-m', type=str, default='license_plate_detection_lpd_yunet_2022may.onnx', help='Usage: Set model path, defaults to license_plate_detection_lpd_yunet_2022may.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('--conf_threshold', type=float, default=0.9, help='Usage: Set the minimum needed confidence for the model to identify a license plate, defaults to 0.9. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold.')
-parser.add_argument('--nms_threshold', type=float, default=0.3, help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3. Suppress bounding boxes of iou >= nms_threshold.')
-parser.add_argument('--top_k', type=int, default=5000, help='Usage: Keep top_k bounding boxes before NMS.')
-parser.add_argument('--keep_top_k', type=int, default=750, help='Usage: Keep keep_top_k bounding boxes after NMS.')
-parser.add_argument('--save', '-s', type=str2bool, default=False, help='Usage: Set “True” to save file with results (i.e. bounding box, confidence level). 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 path to the input image. Omit for using default camera.')
+parser.add_argument('--model', '-m', type=str, default='license_plate_detection_lpd_yunet_2023mar.onnx',
+                    help='Usage: Set model path, defaults to license_plate_detection_lpd_yunet_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('--conf_threshold', type=float, default=0.9,
+                    help='Usage: Set the minimum needed confidence for the model to identify a license plate, defaults to 0.9. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold.')
+parser.add_argument('--nms_threshold', type=float, default=0.3,
+                    help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3. Suppress bounding boxes of iou >= nms_threshold.')
+parser.add_argument('--top_k', type=int, default=5000,
+                    help='Usage: Keep top_k bounding boxes before NMS.')
+parser.add_argument('--keep_top_k', type=int, default=750,
+                    help='Usage: Keep keep_top_k bounding boxes after NMS.')
+parser.add_argument('--save', '-s', action='store_true',
+                    help='Usage: Specify to save file with results (i.e. bounding box, confidence level). 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 visualize(image, dets, line_color=(0, 255, 0), text_color=(0, 0, 255), fps=None):
@@ -57,14 +64,17 @@ def visualize(image, dets, line_color=(0, 255, 0), text_color=(0, 0, 255), fps=N
     return output
 
 if __name__ == '__main__':
+    backend_id = backend_target_pairs[args.backend_target][0]
+    target_id = backend_target_pairs[args.backend_target][1]
+
     # Instantiate LPD-YuNet
     model = LPD_YuNet(modelPath=args.model,
                       confThreshold=args.conf_threshold,
                       nmsThreshold=args.nms_threshold,
                       topK=args.top_k,
                       keepTopK=args.keep_top_k,
-                      backendId=args.backend,
-                      targetId=args.target)
+                      backendId=backend_id,
+                      targetId=target_id)
 
     # If input is an image
     if args.input is not None:
@@ -117,4 +127,3 @@ if __name__ == '__main__':
             cv.imshow('LPD-YuNet Demo', frame)
 
             tm.reset()
-

lpd_yunet.py

@@ -28,12 +28,10 @@ class LPD_YuNet:
     def name(self):
         return self.__class__.__name__
 
-    def setBackend(self, backendId):
+    def setBackendAndTarget(self, backendId, targetId):
         self.backend_id = backendId
-        self.model.setPreferableBackend(self.backend_id)
-
-    def setTarget(self, targetId):
         self.target_id = targetId
+        self.model.setPreferableBackend(self.backend_id)
         self.model.setPreferableTarget(self.target_id)
 
     def setInputSize(self, inputSize):