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

@@ -3,14 +3,15 @@
 YuNet is a light-weight, fast and accurate face detection model, which achieves 0.834(AP_easy), 0.824(AP_medium), 0.708(AP_hard) on the WIDER Face validation set.
 
 Notes:
+
 - Model source: [here](https://github.com/ShiqiYu/libfacedetection.train/blob/a61a428929148171b488f024b5d6774f93cdbc13/tasks/task1/onnx/yunet.onnx).
 - For details on training this model, please visit https://github.com/ShiqiYu/libfacedetection.train.
 - This ONNX model has fixed input shape, but OpenCV DNN infers on the exact shape of input image. See https://github.com/opencv/opencv_zoo/issues/44 for more information.
 
 Results of accuracy evaluation with [tools/eval](../../tools/eval).
 
-| Models      | Easy AP | Medium AP | Hard AP | 
-|-------------|---------|-----------|---------|
+| Models      | Easy AP | Medium AP | Hard AP |
+| ----------- | ------- | --------- | ------- |
 | YuNet       | 0.8498  | 0.8384    | 0.7357  |
 | YuNet quant | 0.7751  | 0.8145    | 0.7312  |
 
@@ -19,11 +20,15 @@ Results of accuracy evaluation with [tools/eval](../../tools/eval).
 ## Demo
 
 Run the following command to try the demo:
+
 ```shell
 # detect on camera input
 python demo.py
 # detect on an image
 python demo.py --input /path/to/image
+
+# get help regarding various parameters
+python demo.py --help
 ```
 
 ### Example outputs

demo.py

@@ -22,25 +22,25 @@ def str2bool(v):
 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"
+help_msg_targets = "Choose 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://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='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).')
-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='face_detection_yunet_2022mar.onnx', help='Path to the model.')
+parser.add_argument('--input', '-i', type=str, help='Usage: Set input to a certain image, omit if using camera.')
+parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2022mar.onnx', help="Usage: Set model type, defaults to 'face_detection_yunet_2022mar.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='Filter out faces of confidence < conf_threshold.')
-parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
-parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
-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('--conf_threshold', type=float, default=0.9, help='Usage: Set the minimum needed confidence for the model to identify a face, defauts 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.')
+parser.add_argument('--top_k', type=int, default=5000, help='Usage: Keep top_k bounding boxes before NMS.')
+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, box_color=(0, 255, 0), text_color=(0, 0, 255), fps=None):