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

@@ -5,35 +5,40 @@ import cv2 as cv
 
 from mp_palmdet import MPPalmDet
 
-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='Hand Detector from MediaPipe')
-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='./palm_detection_mediapipe_2023feb.onnx', help='Usage: Set model path, defaults to palm_detection_mediapipe_2023feb.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('--score_threshold', type=float, default=0.8, help='Usage: Set the minimum needed confidence for the model to identify a palm, defaults to 0.8. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold. An empirical score threshold for the quantized model is 0.49.')
-parser.add_argument('--nms_threshold', type=float, default=0.3, help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3.')
-parser.add_argument('--save', '-s', type=str, 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='./palm_detection_mediapipe_2023feb.onnx',
+                    help='Usage: Set model path, defaults to palm_detection_mediapipe_2023feb.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('--score_threshold', type=float, default=0.8,
+                    help='Usage: Set the minimum needed confidence for the model to identify a palm, defaults to 0.8. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold. An empirical score threshold for the quantized model is 0.49.')
+parser.add_argument('--nms_threshold', type=float, default=0.3,
+                    help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3.')
+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, results, print_results=False, fps=None):
@@ -71,12 +76,15 @@ def visualize(image, results, print_results=False, fps=None):
     return output
 
 if __name__ == '__main__':
+    backend_id = backend_target_pairs[args.backend_target][0]
+    target_id = backend_target_pairs[args.backend_target][1]
+
     # Instantiate MPPalmDet
     model = MPPalmDet(modelPath=args.model,
                       nmsThreshold=args.nms_threshold,
                       scoreThreshold=args.score_threshold,
-                      backendId=args.backend,
-                      targetId=args.target)
+                      backendId=backend_id,
+                      targetId=target_id)
 
     # If input is an image
     if args.input is not None:
@@ -123,4 +131,3 @@ if __name__ == '__main__':
             cv.imshow('MPPalmDet Demo', frame)
 
             tm.reset()
-

mp_palmdet.py

@@ -22,12 +22,10 @@ class MPPalmDet:
     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 _preprocess(self, image):
@@ -35,7 +33,7 @@ class MPPalmDet:
         ratio = min(self.input_size / image.shape[:2])
         if image.shape[0] != self.input_size[0] or image.shape[1] != self.input_size[1]:
             # keep aspect ratio when resize
-            ratio_size = (np.array(image.shape[:2]) * ratio).astype(np.int)
+            ratio_size = (np.array(image.shape[:2]) * ratio).astype(np.int32)
             image = cv.resize(image, (ratio_size[1], ratio_size[0]))
             pad_h = self.input_size[0] - ratio_size[0]
             pad_w = self.input_size[1] - ratio_size[1]
@@ -46,7 +44,7 @@ class MPPalmDet:
             image = cv.copyMakeBorder(image, top, bottom, left, right, cv.BORDER_CONSTANT, None, (0, 0, 0))
         image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
         image = image.astype(np.float32) / 255.0 # norm
-        pad_bias = (pad_bias / ratio).astype(np.int)
+        pad_bias = (pad_bias / ratio).astype(np.int32)
         return image[np.newaxis, :, :, :], pad_bias # hwc -> nhwc
 
     def infer(self, image):