add adapter for OpenCV

videocr/opencv_adapter.py

@@ -0,0 +1,15 @@
+import cv2
+
+
+class Capture:
+    def __init__(self, video_path):
+        self.path = video_path
+
+    def __enter__(self):
+        self.cap = cv2.VideoCapture(self.path)
+        if not self.cap.isOpened():
+            raise IOError('Can not open video {}.'.format(self.path))
+        return self.cap
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.cap.release()

videocr/video.py

@@ -6,6 +6,7 @@ import cv2
 
 from . import constants
 from .models import PredictedFrame, PredictedSubtitle
+from .opencv_adapter import Capture
 
 
 class Video:
@@ -20,13 +21,10 @@ class Video:
 
     def __init__(self, path: str):
         self.path = path
-        v = cv2.VideoCapture(path)
-        if not v.isOpened():
-            raise IOError('can not open video format {}'.format(path))
+        with Capture(path) as v:
             self.num_frames = int(v.get(cv2.CAP_PROP_FRAME_COUNT))
             self.fps = v.get(cv2.CAP_PROP_FPS)
             self.height = int(v.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        v.release()
 
     def run_ocr(self, lang: str, time_start: str, time_end: str,
                 conf_threshold:int, use_fullframe: bool) -> None:
@@ -41,13 +39,12 @@ class Video:
         num_ocr_frames = ocr_end - ocr_start
 
         # get frames from ocr_start to ocr_end
-        v = cv2.VideoCapture(self.path)
+        with Capture(self.path) as v, multiprocessing.Pool() as pool:
             v.set(cv2.CAP_PROP_POS_FRAMES, ocr_start)
             frames = (v.read()[1] for _ in range(num_ocr_frames))
 
             # perform ocr to frames in parallel
-        with Pool() as pool:
-            it_ocr = pool.imap(self._single_frame_ocr, frames, chunksize=10)
+            it_ocr = pool.imap(self._image_to_data, frames, chunksize=10)
             self.pred_frames = [
                 PredictedFrame(i + ocr_start, data, conf_threshold) 
                 for i, data in enumerate(it_ocr)]