9c3d9ba05502b5f553bc8abc44d08e09e1851e68,deeplabcut/pose_estimation_tensorflow/predict_multianimal.py,,GetPoseandCostsF,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,126

Before Change

                    outputs,
                    c_engine=c_engine,
                )
                for l in range(batch_ind):
                    // pose = predict.getposeNP(frames,dlc_cfg, sess, inputs, outputs)
                    // PredicteData[batch_num*batchsize:(batch_num+1)*batchsize, :] = pose
                    PredicteData[
                        "frame" + str(batch_num * batchsize + l).zfill(strwidth)
                    ] = D[l]
            break
        counter += 1
    cap.close()
    pbar.close()

After Change

        if counter % step == 0:
            pbar.update(step)
        frame = cap.read_frame(crop=cfg["cropping"])
        inds = []
        if frame is not None:
            frames[batch_ind] = img_as_ubyte(frame)
            inds.append(counter)
            if batch_ind == batchsize - 1:
                // PredicteData["frame"+str(counter)]=predict.get_detectionswithcosts(frame, dlc_cfg, sess, inputs, outputs, outall=False,nms_radius=dlc_cfg.nmsradius,det_min_score=dlc_cfg.minconfidence)
                D = predict.get_batchdetectionswithcosts(
                    frames,
                    dlc_cfg,
                    dist_grid,
                    batchsize,
                    num_joints,
                    num_idchannel,
                    stride,
                    halfstride,
                    det_min_score,
                    sess,
                    inputs,
                    outputs,
                )
                for ind, data in zip(inds, D):
                    PredicteData["frame" + str(ind).zfill(strwidth)] = data
                batch_ind = 0
                inds.clear()
                batch_num += 1
            else:
                batch_ind += 1
        elif counter >= nframes:
            if batch_ind > 0:
                // pose = predict.getposeNP(frames, dlc_cfg, sess, inputs, outputs) //process the whole batch (some frames might be from previous batch!)
                // PredicteData[batch_num*batchsize:batch_num*batchsize+batch_ind, :] = pose[:batch_ind,:]
                D = predict.get_batchdetectionswithcosts(
                    frames,
                    dlc_cfg,
                    dist_grid,
                    batchsize,
                    num_joints,
                    num_idchannel,
                    stride,
                    halfstride,
                    det_min_score,
                    sess,
                    inputs,
                    outputs,
                    c_engine=c_engine,
                )
                for ind, data in zip(inds, D):
                    PredicteData["frame" + str(ind).zfill(strwidth)] = data
            break
        counter += 1

    cap.close()

Instances