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main_report.py
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main_report.py
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# -------------------------------------------------------------------------------
# Name: main
# Purpose: Testing the package pySaliencyMap
#
# Author: Akisato Kimura <akisato@ieee.org>
#
# Created: May 4, 2014
# Copyright: (c) Akisato Kimura 2014-
# Licence: All rights reserved
# -------------------------------------------------------------------------------
import cv2
import matplotlib.pyplot as plt
import matplotlib
# matplotlib.use('TkAgg')
# matplotlib.use('Agg')
from scipy import ndimage, misc
import pySaliencyMap
import time
import numpy as np
from generateChannels import generateChannels
from makeBorderOwnership import makeBorderOwnership
# from salienpy.salienpy.commons import minmaxnormalization
# from ittiNorm import ittiNorm
# from numba import vectorize, cuda
from multiprocessing import Process
from makeColors import makeColors
from makeDefaultParams import makeDefaultParams
from computeTemporalFiltering import computeTemporalFiltering
from normalizeImage import normalizeImage
# main
# @vectorize(['float32(float32)'],target='cuda')
# def static(img):
# # read
# # img = cv2.imread('test3.jpg')
# # initialize
# img = np.transpose(img,[1,2,0])
# imgsize = img.shape
# img_width = imgsize[1]
# img_height = imgsize[0]
# sm = pySaliencyMap.pySaliencyMap(img_width, img_height)
# sm = sm.SMGetSM(img)
# # computation
# # start = time.time()
# # for i in range(100):
# # sal_map = []
# # for i in range(3):
# # sal_map.append()
#
#
#
# return sm
import h5py
from ComputeTemporalFilter_jam import ComputeTemporalFilter_jam
import tensorflow as tf
from makeTemporalFilter import makeTemporalFilter
#tf.enable_eager_execution()
import cv2
import matplotlib.pyplot as plt
if __name__ == '__main__':
params = makeDefaultParams(1e5)
cap = cv2.VideoCapture(1)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 300)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 300)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# cap= cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)308, height=(int)308,format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink")
sm = pySaliencyMap.pySaliencyMap(height, width)
r_s = np.reshape(makeTemporalFilter('strong_t3'), (3, 1, 1, 1))
r_w = np.reshape(makeTemporalFilter('weak_t6'), (6, 1, 1, 1))
imgs = np.zeros((height, width, 3, 3))
print(r_s.shape)
video = np.zeros((6, height, width, 3))
start = time.time()
for i in range(6):
ret, frame = cap.read()
print(np.asarray(frame).shape)
video[i, :, :, :] = frame
imgs = np.zeros((video[0].shape[0], video[0].shape[1], video[0].shape[2], len(params['channels'])))
# plt.ion()
salmap = np.zeros((height, width))
# plt.title("Histogram")
# cv2.namedWindow('frame')
while (True):
start = time.time()
video = np.roll(video, -1, axis=0)
ret, frame = cap.read()
video[5, :, :, :] = frame
temp_out_strong, temp_out_weak = ComputeTemporalFilter_jam(video, r_s, r_w)
imgs[:, :, :, 0] = normalizeImage(temp_out_strong)
imgs[:, :, :, 1] = normalizeImage(temp_out_weak)
imgs[:, :, :, 2] = normalizeImage(video[2])
[inp, in_orient, R, G, B, Y] = makeColors(imgs)
salmap = sm.sal_map(R, G, B, inp)
cv2.imshow('orig',frame)
# cv2.imshow('frame', cv2.applyColorMap((((-1 * cv2.resize(salmap, (0,0), fx=3, fy=3)) / 32) * 255).astype(np.uint8), cv2.COLORMAP_JET))
cv2.imshow('frame',cv2.applyColorMap((((-1 * salmap) / 32) * 255).astype(np.uint8),cv2.COLORMAP_JET))
# cv2.imshow('frame',cv2.medianBlur(cv2.resize((cv2.applyColorMap((((-1 * salmap) / 32) * 255).astype(np.uint8), cv2.COLORMAP_JET)), (width, height), interpolation=cv2.INTER_LINEAR),10))
print("time", 1 / (time.time() - start))
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()