fastai_pipeline.py.backup

import torchvision
import numpy as np
import time
from fastai.vision import *



#!/usr/bin/env python
import rospy

import sys

try:
    sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
except:
    pass

import cv2 as cv
import torch
from torch.autograd.variable import Variable
from torchvision.transforms import Normalize
import sys
import rospy
from std_msgs.msg import String
from std_msgs.msg import Int32
import numpy as np
from PIL import Image

imagenet_stats = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]

# try:
#     sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')   # It causes cv2 import error
# except:
#     print('yeah!')


def preprocess(images):
    images = torch.unsqueeze(torch.from_numpy(images),dim=0)
    images = images.float()
    images = Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(images)
    return images#.half()

def get_subtracted(images):
    images = images.view(images.size(0)/2, 2, 3, 480, 640)
    images1, images2 = images[:,0], images[:,1]
    return images1-images2

def get_masked(img, mask):
    masked_image = np.multiply(img, mask/255)
    return masked_image

def getnp(mat):
    mat = mat.cpu()
    return mat.detach().numpy()

def get_control():

    cap = cv.VideoCapture(1)
    while(True):

        ret, frame = cap.read()
        cv.imwrite('buffer.jpg', frame)
        test_image = open_image('buffer.jpg')
        img_segment = seg_learn.predict(test_image)[0]

        masked = np.multiply(test_image.data, img_segment.data)
        torchvision.utils.save_image(masked, 'bh_test_masked.jpg')

        masked = open_image('bh_test_masked.jpg')
        action = bh_learn.predict(masked)
        #ite(masked_image, 'file.jpg')
            #action = bh_model(torch.tensor(masked_image))#.half())
            # action = bh_model(get_subtracted(images))
            #
            # #segmentation maps
            # action = bh_model(seg_maps)
            # action = bh_model(get_subtracted(seg_maps))
            #
            # #seg_maps + images
            # action = bh_model(masked_image(images, seg_maps))
            # action = bh_model(get_subtracted((masked_image(images, seg_maps))))

            #_, action = torch.max(torch.nn.Softmax(dim=1)(action),1)
            #action = action.numpy()
            #print(action[0])

        # publish action on a node
        pub = rospy.Publisher('in_put', Int32 , queue_size=10)
        # rate = rospy.Rate(100) # 10hz
        try:#if not rospy.is_shutdown():
            print(int(action[0]))
            # print(str(action[0]))
            pub.publish(int(action[0]))
                # rate.sleep()
        except:
            print('couldnot publish')



if __name__ == '__main__':
    #fastai model
    seg_learner_path = './models/segmentation_model/'
    bh_learner_path = './models/bh_cloning_model/'
    seg_learn = load_learner(seg_learner_path).to_fp32()
    bh_learn = load_learner(bh_learner_path).to_fp32()

    # segmentation_model = torch.load('model/unet_cpu.pth',map_location='cpu')
    # segmentation_model = segmentation_model.float()
    # bh_model = torch.load('model/masked_img_model_cpu.pth',map_location='cpu')
    # bh_model = bh_model.float()
    print('Press Ctrl+C for exiting')
    rospy.init_node('detector', anonymous=True)
    rospy.Subscriber("img_raw", String, get_control)
    get_control()
    print('reached')
    rospy.spin()