feat(#47): finish one-step API

stagesepx/cli.py

@@ -4,22 +4,45 @@
 from stagesepx.cutter import VideoCutter
 from stagesepx.classifier import SVMClassifier
 from stagesepx.reporter import Reporter
+from stagesepx import toolbox
 
 
 class TerminalCli(object):
-    """ this is a client for stagesepx, for easier usage. """
+    """
+    this is a client for stagesepx, for easier usage.
+    for flexible usage, you 'd better use the script way.
+    """
+
+    @toolbox.arg_printer
+    def one_step(self,
+                 video_path: str,
+                 output_path: str = None,
+                 frame_count: int = 5,
+                 compress_rate: float = 0.2,
+                 limit: int = None):
+        """
+        one step => cut, classifier, draw
+
+        :param video_path: your video path
+        :param output_path: output path (dir)
+        :param frame_count: default to 5, and finally you will get 5 frames for each range
+        :param compress_rate: before_pic * compress_rate = after_pic. default to 0.2
+        :param limit: ignore some ranges which are too short, 5 means ignore stable ranges which length < 5
+        :return:
+        """
 
-    def one_step(self, video_path: str, output_path: str = None):
         # --- cutter ---
         cutter = VideoCutter()
-        res = cutter.cut(video_path)
-        stable, unstable = res.get_range()
+        res = cutter.cut(video_path, compress_rate=compress_rate)
+        stable, unstable = res.get_range(limit=limit)
 
-        data_home = res.pick_and_save(stable, 5, to_dir=output_path)
+        data_home = res.pick_and_save(
+            stable,
+            frame_count,
+            to_dir=output_path)
 
         # --- classify ---
-
-        cl = SVMClassifier()
+        cl = SVMClassifier(compress_rate=compress_rate)
         cl.load(data_home)
         cl.train()
         classify_result = cl.classify(video_path, stable)

stagesepx/toolbox.py

@@ -5,6 +5,8 @@
 import typing
 import math
 import numpy as np
+from loguru import logger
+from functools import wraps
 from base64 import b64encode
 from skimage.filters import threshold_otsu
 from skimage.measure import compare_ssim as origin_compare_ssim
@@ -229,6 +231,15 @@ def np2b64str(frame: np.ndarray) -> str:
     return b64encode(buffer).decode()
 
 
+def arg_printer(func: typing.Callable):
+    @wraps(func)
+    def _wrapper(*args, **kwargs):
+        logger.debug(f'function {func.__name__} args: {args}, kwargs: {kwargs}')
+        return func(*args, **kwargs)
+
+    return _wrapper
+
+
 if __name__ == '__main__':
     t = cv2.imread('../1.png', cv2.IMREAD_GRAYSCALE)
     t = sharpen_frame(t)