Expand Up @@ -262,7 +262,9 @@ def KmeansbasedFrameselectioncv2( if batchsize > nframes: batchsize = nframes // 2
allocated = False ny_ = np.round(ny * ratio).astype(int) nx_ = np.round(nx * ratio).astype(int) DATA = np.empty((nframes, ny_, nx_ * 3 if color else nx_)) if len(Index) >= numframes2pick: if ( np.mean(np.diff(Index)) > 1 Expand All @@ -282,13 +284,6 @@ def KmeansbasedFrameselectioncv2( interpolation=cv2.INTER_NEAREST, ) ) # color trafo not necessary; lack thereof improves speed. if ( not allocated ): #'DATA' not in locals(): #allocate memory in first pass DATA = np.empty( (nframes, np.shape(image)[0], np.shape(image)[1] * 3) ) allocated = True DATA[counter, :, :] = np.hstack( [image[:, :, 0], image[:, :, 1], image[:, :, 2]] ) Expand All @@ -306,13 +301,6 @@ def KmeansbasedFrameselectioncv2( interpolation=cv2.INTER_NEAREST, ) ) # color trafo not necessary; lack thereof improves speed. if ( not allocated ): #'DATA' not in locals(): #allocate memory in first pass DATA = np.empty( (nframes, np.shape(image)[0], np.shape(image)[1]) ) allocated = True DATA[counter, :, :] = np.mean(image, 2) else: print("Extracting and downsampling...", nframes, " frames from the video.") Expand All @@ -329,13 +317,6 @@ def KmeansbasedFrameselectioncv2( interpolation=cv2.INTER_NEAREST, ) ) # color trafo not necessary; lack thereof improves speed. if ( not allocated ): #'DATA' not in locals(): #allocate memory in first pass DATA = np.empty( (nframes, np.shape(image)[0], np.shape(image)[1] * 3) ) allocated = True DATA[counter, :, :] = np.hstack( [image[:, :, 0], image[:, :, 1], image[:, :, 2]] ) Expand All @@ -352,13 +333,6 @@ def KmeansbasedFrameselectioncv2( interpolation=cv2.INTER_NEAREST, ) ) # color trafo not necessary; lack thereof improves speed. if ( not allocated ): #'DATA' not in locals(): #allocate memory in first pass DATA = np.empty( (nframes, np.shape(image)[0], np.shape(image)[1]) ) allocated = True DATA[counter, :, :] = np.mean(image, 2)
print("Kmeans clustering ... (this might take a while)") Expand Down