example_tiny_ophys_inference_single.py

import os
from datetime import datetime
from scipy.io import loadmat


def inference(path,tag,sess):

    import os
    import scipy.io as sio
    from deepinterpolation.generic import JsonSaver, ClassLoader
    from datetime import datetime

    startTime=datetime.now()

    generator_param = {}
    inferrence_param = {}

    # We are reusing the data generator for training here.
    generator_param["type"] = "generator"
    generator_param["name"] = "SingleTifGenerator"
    generator_param["pre_post_frame"] = 30
    generator_param["pre_post_omission"] = 0
    generator_param[
        "steps_per_epoch"
    ] = -1  # No steps necessary for inference as epochs are not relevant. -1 deactivate it.

    generator_param["train_path"] = path
    #X:/Projects/Perirhinal/Animals/pr020/2P/pr020-1/PreProcess/A1_Ch0/A1_Ch0_15-31-28.mat"
    #os.path.join(
        #pathlib.Path(__file__).parent.absolute(),
        #"..",
        #"sample_data",
        #"A0_Ch0_16-44-59.mat",
    #)

    generator_param["batch_size"] = 5
    generator_param["start_frame"] = 0
    generator_param["end_frame"] = -1  # -1 to go until the end.
    generator_param[
        "randomize"
    ] = 0  # This is important to keep the order and avoid the randomization used during training


    inferrence_param["type"] = "inferrence"
    inferrence_param["name"] = "core_inferrence"

    # Replace this path to where you stored your model
    inferrence_param[
        "model_path"
    ] = "/usr3/bustaff/dlamay/deepinterpolation/2021_03_22_13_24_transfer_mean_squared_error_rigid_test_train_bad.h5"

    # Replace this path to where you want to store your output file
    #inferrence_param[
        #"output_file"
    #] = "X:/Projects/Perirhinal/deepinterpolation/trained_models/ophys_tiny_continuous_deep_interpolation_pr020-28_A1_Ch0_10-16-03_transfer_rigid_test_train_bad.h5"

    inferrence_param["mat_file"] = path.replace(".mat","_dp.mat")


    jobdir = "/usr3/bustaff/dlamay/deepinterpolation/"

    try:
        os.mkdir(jobdir)
    except:
        print("folder already exists")

    #tag = re.search('\\\\{4}(.+?).mat',path).group(1)


    path_generator = os.path.join(jobdir, "generator_" + sess + tag + ".json")
    json_obj = JsonSaver(generator_param)
    json_obj.save_json(path_generator)

    path_infer = os.path.join(jobdir, "inferrence_" + sess + tag + ".json")
    json_obj = JsonSaver(inferrence_param)
    json_obj.save_json(path_infer)

    generator_obj = ClassLoader(path_generator)
    data_generator = generator_obj.find_and_build()(path_generator)


    inferrence_obj = ClassLoader(path_infer)
    inferrence_class = inferrence_obj.find_and_build()(path_infer, data_generator)

    # Except this to be slow on a laptop without GPU. Inference needs parallelization to be effective.


    out = inferrence_class.run()
    framedata=data_generator.list_samples[0:len(data_generator)*5]
    matdata = np.ascontiguousarray(out)
    matdata = matdata[:,data_generator.a:512-data_generator.a,data_generator.b:512-data_generator.b]
    matsavedata = np.swapaxes(matdata, 0, 2)
    matsavedata = np.swapaxes(matsavedata, 0, 1)
    sio.savemat(path.replace(".mat","_dp.mat"), mdict={'inference_data':matsavedata, 'frame_id':framedata})

    os.remove(path_generator)
    os.remove(path_infer)



    print(datetime.now() - startTime)

def inference2(path,start,end,tag,sess):
    import os
    from deepinterpolation.generic import JsonSaver, ClassLoader
    import numpy as np
    import scipy.io as sio
    from scipy.io import loadmat


    generator_param = {}
    inferrence_param = {}

    # We are reusing the data generator for training here.
    generator_param["type"] = "generator"
    generator_param["name"] = "SingleTifGenerator"
    generator_param["pre_post_frame"] = 30
    generator_param["pre_post_omission"] = 0
    generator_param[
        "steps_per_epoch"
    ] = -1  # No steps necessary for inference as epochs are not relevant. -1 deactivate it.

    generator_param["train_path"] = path
    #X:/Projects/Perirhinal/Animals/pr020/2P/pr020-1/PreProcess/A1_Ch0/A1_Ch0_15-31-28.mat"
    #os.path.join(
        #pathlib.Path(__file__).parent.absolute(),
        #"..",
        #"sample_data",
        #"A0_Ch0_16-44-59.mat",
    #)

    generator_param["batch_size"] = 1
    generator_param["start_frame"] = start
    generator_param["end_frame"] = end # -1 to go until the end.
    generator_param[
        "randomize"
    ] = 0  # This is important to keep the order and avoid the randomization used during training


    inferrence_param["type"] = "inferrence"
    inferrence_param["name"] = "core_inferrence"

    # Replace this path to where you stored your model
    inferrence_param[
        "model_path"
    ] = "/usr3/bustaff/dlamay/deepinterpolation/2021_03_22_13_24_transfer_mean_squared_error_rigid_test_train_bad.h5"

    # Replace this path to where you want to store your output file
    #inferrence_param[
        #"output_file"
    #] = "X:/Projects/Perirhinal/deepinterpolation/trained_models/ophys_tiny_continuous_deep_interpolation_pr020-28_A1_Ch0_10-16-03_transfer_rigid_test_train_bad.h5"

    inferrence_param["mat_file"] = path.replace(".mat","_dp.mat")

    jobdir = "/usr3/bustaff/dlamay/deepinterpolation"

    try:
        os.mkdir(jobdir)
    except:
        print("folder already exists")


    path_generator = os.path.join(jobdir, "generator2_" + sess + tag +".json")

    json_obj = JsonSaver(generator_param)
    json_obj.save_json(path_generator)

    path_infer = os.path.join(jobdir, "inferrence2_" + sess + tag + ".json")
    json_obj = JsonSaver(inferrence_param)
    json_obj.save_json(path_infer)

    generator_obj = ClassLoader(path_generator)
    data_generator = generator_obj.find_and_build()(path_generator)


    inferrence_obj = ClassLoader(path_infer)
    inferrence_class = inferrence_obj.find_and_build()(path_infer, data_generator)


    # Except this to be slow on a laptop without GPU. Inference needs parallelization to be effective.



    old=loadmat(path.replace(".mat","_dp.mat"))["inference_data"]
    old_id = loadmat(path.replace(".mat","_dp.mat"))["frame_id"]
    new_id = data_generator.list_samples[0:len(data_generator)*5]
    framedata = np.concatenate([np.squeeze(old_id),new_id])
    out = inferrence_class.run()
    matdata = np.ascontiguousarray(out)
    matdata = matdata[:,data_generator.a:512-data_generator.a,data_generator.b:512-data_generator.b]
    old = np.ascontiguousarray(np.swapaxes(old, 1, 2))
    old = np.ascontiguousarray(np.swapaxes(old, 0, 1))
    matsavedata=np.concatenate([old,matdata],0)
    matsavedata = np.swapaxes(matsavedata, 0, 2)
    matsavedata = np.swapaxes(matsavedata, 0, 1)
    sio.savemat(path.replace(".mat","_dp.mat"), mdict={'inference_data':matsavedata,
                                                        'frame_id':framedata})


    os.remove(path_generator)
    os.remove(path_infer)


import sys
import numpy as np
import glob
import requests
import json
from tqdm import tqdm
import tensorflow as tf








path = "your path to video here"



sess = (path.split('-'))[1].split('/')[0]
tag=path.split("/")[-1].replace('.mat','')

print('start pass 1')
startTime=datetime.now()
inference(path,tag,sess)
print(datetime.now() - startTime)

print('start pass 2')
mat_file = loadmat(path)['motion_corrected']
dp_file= loadmat(path.replace('.mat','_dp.mat'))['inference_data']
start=int(np.floor(float(mat_file.shape[2]-60)) / 5)*5 #to grab extra frames missed by batch size
end = mat_file.shape[2]-1
if (dp_file.shape[2] != mat_file.shape[2]-60):
    inference2(path,start,end,tag,sess)