Merge pull request #95 from Satgoy152:adding-doc

Improved help messages for demo programs (#95)
- Added Demo Documentation
- Updated help messages
- Changed exception link

README.md

@@ -1,20 +1,25 @@
 # Palm detector from MediaPipe Handpose
 
 This model detects palm bounding boxes and palm landmarks, and is converted from Tensorflow-JS to ONNX using following tools:
+
 - tfjs to tf_saved_model:  https://github.com/patlevin/tfjs-to-tf/
 - tf_saved_model to ONNX: https://github.com/onnx/tensorflow-onnx
 - simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)
 
-Also note that the model is quantized in per-channel mode with [Intel's neural compressor](https://github.com/intel/neural-compressor), which gives better accuracy but may lose some speed.
+Also note that the model is quantized in per-channel mode with [Intel's neural compressor](https://github.com/intel/neural-compressor), which gives better accuracy but may lose some speed.
 
 ## Demo
 
 Run the following commands to try the demo:
+
 ```bash
 # detect on camera input
 python demo.py
 # detect on an image
 python demo.py -i /path/to/image
+
+# get help regarding various parameters
+python demo.py --help
 ```
 
 ### Example outputs

demo.py

@@ -23,17 +23,17 @@ try:
     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://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.')
+    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.')
 
 parser = argparse.ArgumentParser(description='Hand Detector from MediaPipe')
-parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
-parser.add_argument('--model', '-m', type=str, default='./palm_detection_mediapipe_2022may.onnx', help='Path to the model.')
+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_2022may.onnx', help='Usage: Set model path, defaults to palm_detection_mediapipe_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('--score_threshold', type=float, default=0.99, help='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='Suppress bounding boxes of iou >= nms_threshold.')
-parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
-parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
+parser.add_argument('--score_threshold', type=float, default=0.99, help='Usage: Set the minimum needed confidence for the model to identify a palm, defaults to 0.99. 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.')
 args = parser.parse_args()
 
 def visualize(image, results, print_results=False, fps=None):