Multiprocessing (failed or partial) attempt

darknet.py

@@ -15,6 +15,17 @@
 import numpy as np
 
 
+class PickleableStructure(ct.Structure):
+ def _compute_state(self):
+ raise NotImplementedError
+
+ def __reduce__(self):
+ return self.__class__, (), self._compute_state()
+
+ def __setstate__(self, state):
+ raise NotImplementedError
+
+
 class BOX(ct.Structure):
  _fields_ = (
  ("x", ct.c_float),
@@ -59,14 +70,21 @@ class DETNUMPAIR(ct.Structure):
 DETNUMPAIRPtr = ct.POINTER(DETNUMPAIR)
 
 
-class IMAGE(ct.Structure):
+class IMAGE(PickleableStructure):
  _fields_ = (
  ("w", ct.c_int),
  ("h", ct.c_int),
  ("c", ct.c_int),
  ("data", FloatPtr),
  )
 
+ def _compute_state(self):
+ return self.w, self.h, self.c, self.data[:self.w * self.h * self.c]
+
+ def __setstate__(self, state):
+ self.w, self.h, self.c = state[:3]
+ self.data = ct.cast((self.data._type_ * (self.w * self.h * self.c))(*state[-1]), FloatPtr)
+
 
 class METADATA(ct.Structure):
  _fields_ = (
@@ -261,7 +279,20 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45
 elif os.name == "nt":
  cwd = os.path.dirname(__file__)
  os.environ["PATH"] = os.path.pathsep.join((cwd, os.environ["PATH"]))
- lib = ct.CDLL("darknet.dll", ct.RTLD_GLOBAL)
+ #lib = ct.CDLL("darknet.dll", ct.RTLD_GLOBAL)
+ os.add_dll_directory(os.getcwd())
+ _GPU = 1
+ if _GPU:
+ nvs = (
+ r"F:\Install\pc064\NVidia\CUDAToolkit\11.3\bin",
+ r"F:\Install\pc064\NVidia\cuDNN\8.2.0-CUDA11\bin",
+ )
+ for nv in nvs:
+ os.add_dll_directory(nv)
+ os.environ["PATH"] += ";" + ";".join(nvs) # ! Strangely, crashes (can't find cudnn_ops_infer64_8.dll) without !
+ lib = ct.CDLL("darknet_gpu.dll", ct.RTLD_GLOBAL)
+ else:
+ lib = ct.CDLL("darknet_nogpu.dll", ct.RTLD_GLOBAL)
 else:
  lib = None # Intellisense
  print("Unsupported OS")

darknet_video.py

@@ -5,6 +5,7 @@
 import darknet
 import argparse
 import threading
+import multiprocessing as mp
 import queue
 
 
@@ -26,6 +27,8 @@ def parser():
  help="path to data file")
  parser.add_argument("--thresh", type=float, default=.25,
  help="remove detections with confidence below this value")
+ parser.add_argument("--multiprocess", action="store_true",
+ help="use processes instead of threads")
  return parser.parse_args()
 
 
@@ -107,7 +110,7 @@ def convert4cropping(image, bbox, preproc_h, preproc_w):
 
 def video_capture(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue, preproc_h, preproc_w):
  cap = cv2.VideoCapture(input_path)
- while cap.isOpened() and not stop_flag.is_set():
+ while cap.isOpened() and stop_flag.empty():
  ret, frame = cap.read()
  if not ret:
  break
@@ -118,30 +121,38 @@ def video_capture(stop_flag, input_path, raw_frame_queue, preprocessed_frame_que
  img_for_detect = darknet.make_image(preproc_w, preproc_h, 3)
  darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
  preprocessed_frame_queue.put(img_for_detect)
- stop_flag.set()
+ stop_flag.put(None)
  cap.release()
+ print("video_capture end:", os.getpid())
 
 
 def inference(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue,
- network, class_names, threshold):
- while not stop_flag.is_set():
+ config_file, data_file, weights_file, batch_size, threshold, ext_output):
+ network, class_names, _ = darknet.load_network(
+ config_file,
+ data_file,
+ weights_file,
+ batch_size=batch_size)
+ while stop_flag.empty():
  darknet_image = preprocessed_frame_queue.get()
  prev_time = time.time()
  detections = darknet.detect_image(network, class_names, darknet_image, thresh=threshold)
  fps = 1 / (time.time() - prev_time)
  detections_queue.put(detections)
  fps_queue.put(int(fps))
  print("FPS: {:.2f}".format(fps))
- darknet.print_detections(detections, args.ext_output)
+ #darknet.print_detections(detections, ext_output)
  darknet.free_image(darknet_image)
+ darknet.free_network_ptr(network)
+ print("inference end:", os.getpid())
 
 
-def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid_w):
+def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid_w, out_filename, dont_show, class_colors):
  random.seed(3) # deterministic bbox colors
  raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue = queues
- video = set_saved_video(args.out_filename, (vid_w, vid_h), input_video_fps)
+ video = set_saved_video(out_filename, (vid_w, vid_h), input_video_fps)
  fps = 1
- while not stop_flag.is_set():
+ while stop_flag.empty():
  frame = raw_frame_queue.get()
  detections = detections_queue.get()
  fps = fps_queue.get()
@@ -151,13 +162,13 @@ def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid
  bbox_adjusted = convert2original(frame, bbox, preproc_h, preproc_w)
  detections_adjusted.append((str(label), confidence, bbox_adjusted))
  image = darknet.draw_boxes(detections_adjusted, frame, class_colors)
- if not args.dont_show:
+ if not dont_show:
  cv2.imshow("Inference", image)
- if args.out_filename is not None:
+ if out_filename is not None:
  video.write(image)
  if cv2.waitKey(fps) == 27:
  break
- stop_flag.set()
+ stop_flag.put(None)
  video.release()
  cv2.destroyAllWindows()
  timeout = 1 / (fps if fps > 0 else 0.5)
@@ -166,18 +177,22 @@ def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid
  q.get(block=True, timeout=timeout)
  except queue.Empty:
  pass
+ print("drawing end:", os.getpid())
 
 
 if __name__ == "__main__":
  args = parser()
  check_arguments_errors(args)
- network, class_names, class_colors = darknet.load_network(
+ batch_size = 1
+ network, class_names, class_colors = darknet.load_network( # Load network twice :(
  args.config_file,
  args.data_file,
  args.weights,
- batch_size=1)
+ batch_size=batch_size)
  darknet_width = darknet.network_width(network)
  darknet_height = darknet.network_height(network)
+ darknet.free_network_ptr(network)
+ del network
  input_path = str2int(args.input)
  cap = cv2.VideoCapture(input_path) # Open video twice :(
  video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
@@ -186,9 +201,14 @@ def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid
  cap.release()
  del cap
 
- ExecUnit = threading.Thread
- Queue = queue.Queue
- stop_flag = threading.Event()
+ if args.multiprocess:
+ ExecUnit = mp.Process
+ Queue = mp.Queue
+ else:
+ ExecUnit = threading.Thread
+ Queue = queue.Queue
+
+ stop_flag = Queue()
 
  raw_frame_queue = Queue()
  preprocessed_frame_queue = Queue(maxsize=1)
@@ -199,15 +219,19 @@ def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid
  ExecUnit(target=video_capture, args=(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue,
  darknet_height, darknet_width)),
  ExecUnit(target=inference, args=(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue,
- network, class_names, args.thresh)),
+ args.config_file, args.data_file, args.weights, batch_size, args.thresh,
+ args.ext_output)),
  ExecUnit(target=drawing, args=(stop_flag, video_fps,
  (raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue),
- darknet_height, darknet_width, video_height, video_width)),
+ darknet_height, darknet_width, video_height, video_width,
+ args.out_filename, args.dont_show, class_colors)),
  )
  for exec_unit in exec_units:
  exec_unit.start()
+ print("------- EXEC UNIT:", ExecUnit)
  for exec_unit in exec_units:
  exec_unit.join()
+ print("------- EXEC UNIT:", ExecUnit)
 
  print("\nDone.")