removed multiprocessing

algorithm/predict_salamander_abdomen.py

@@ -3,11 +3,9 @@ import time
 import numpy as np
 from pathlib import Path
 from skimage.util import img_as_ubyte
-from multiprocessing import Process
-from multiprocessing import Manager
 
 def run_prediction_dlc(config: str,image: np.ndarray,shuffle: int =1,
-                trainingsetindex: int =0,gputouse: int =None,return_dict: list = None):
+                trainingsetindex: int =0,gputouse: int =None):
     from deeplabcutcore.pose_estimation_tensorflow.nnet import predict
     from deeplabcutcore.pose_estimation_tensorflow.config import load_config
     from tensorflow.python.framework.ops import reset_default_graph
@@ -107,38 +105,6 @@ def run_prediction_dlc(config: str,image: np.ndarray,shuffle: int =1,
     # device.reset()
 
     # tf.Graph.reset_default_graph()
-    sess.close()
-    return_dict.append(PredictedData[0])
-    return_dict.append(nframes)
-    return_dict.append(nx)
-    return_dict.append(ny)
-#    return PredictedData[0], nframes, nx, ny
-
-
-def get_prediction_dlc(config,image,shuffle=1,
-                trainingsetindex=0,gputouse=None):
-    """
-    Output: Predictions of the salamander.
-
-    Parameters
-    ----------
-    config : string
-        Full path of the config.yaml file as a string.
-
-
-    shuffle: int, optional
-        An integer specifying the shuffle index of the training dataset used for training the network. The default is 1.
-
-    trainingsetindex: int, optional
-        Integer specifying which TrainingsetFraction to use. By default the first (note that TrainingFraction is a list in config.yaml).
-
-    gputouse: int, optional. Natural number indicating the number of your GPU (see number in nvidia-smi). If you do not have a GPU put None.
-    """
-    return_dict = Manager().list()
-    # return_dict =  multiprocessing.Value("d", [], lock=False)
-    args = (config, image, shuffle,
-    		trainingsetindex, gputouse, return_dict)
-    p = Process(target=run_prediction_dlc, args=args)
-    p.start()
-    p.join()
-    return return_dict[0], return_dict[1], return_dict[2], return_dict[3]
+    return PredictedData[0], nframes, nx, ny
+
+

algorithm/straighten_with_dlc.py

@@ -67,7 +67,7 @@ def straighten(image):
 			factor = MAX_DEEPLABCUT_IMAGE_SIZE / larger_dim
 			cropped_image = cv2.resize(cropped_image, None, fx=factor, fy=factor, interpolation=cv2.INTER_CUBIC)
 
-		prediction, _, x, y = psa.get_prediction_dlc(config=abs_path_dlc_config, image=cropped_image, gputouse=GPUID)
+		prediction, _, x, y = psa.run_prediction_dlc(config=abs_path_dlc_config, image=cropped_image, gputouse=GPUID)
 
 		# points is an array of coordinates to use to straighten the image:
 		# We use the prediction from deeplabcut for the points. There are two things to take note of.