Esempio n. 1
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    def test_read_img_PIL_pixels(self):

        img = r.read_img_PIL(self.path_img1)

        for ch in range(3):
            for row in range(4):
                for col in range(2):
                    assert_equal(img[ch][row][col], self.img1[row][col][ch])
Esempio n. 2
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    def test_read_img_PIL_pixels(self):

        img = r.read_img_PIL(self.path_img1)

        for ch in range(3):
            for row in range(4):
                for col in range(2):
                    assert_equal(img[ch][row][col], self.img1[row][col][ch])
Esempio n. 3
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    def test_read_img_PIL_subtract_mean(self):

        m = np.array((1., 2. , 3.))
        img = r.read_img_PIL(self.path_img1, mean=m)

        for ch in range(3):
            for row in range(4):
                for col in range(2):
                    assert_equal(img[ch][row][col], self.img1[row][col][ch] - m[ch])
Esempio n. 4
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    def test_read_img_PIL_subtract_mean(self):

        m = np.array((1., 2., 3.))
        img = r.read_img_PIL(self.path_img1, mean=m)

        for ch in range(3):
            for row in range(4):
                for col in range(2):
                    assert_equal(img[ch][row][col],
                                 self.img1[row][col][ch] - m[ch])
Esempio n. 5
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def main(args):
    
    caffe.set_mode_cpu()
    
    # load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
    path_img = '/home/kashefy/data/VOCdevkit/VOC2012/JPEGImagesX/2008_000015.jpg'
    
    bgr_mean = np.array((104.00698793,116.66876762,122.67891434))
    im = Image.open(path_img)
    in_ = np.array(im, dtype=np.float32)
    in_ = in_[:,:,::-1]
    print in_.shape
    print in_
    in_ -= bgr_mean
    print in_
    in_ = in_.transpose((2,0,1))
    
    in_ = read_img_PIL(path_img, mean=bgr_mean)
    
    print 'in_'
    print in_[0, 0, 0:6]
    print in_[1, 0, 0:6]
    print in_[2, 0, 0:6]
    
    in2 = read_img_cv2(path_img, mean=bgr_mean)
    print in2.shape
    #in2[0, :, :] -= 104.00698793
    #in2[1, :, :] -= 116.66876762
    #in2[2, :, :] -= 122.67891434
    
    print in2[0, 0, 0:6]
    print in2[1, 0, 0:6]
    print in2[2, 0, 0:6]
    
    print np.all(in_ == in2)
    print in_[in_ != in2]
    print in2[in_ != in2]
    return 0
        
    # load net
    path_model = '/home/kashefy/data/models/fcn_segm/fcn-32s-Pascal-context/deploy.prototxt'
    path_weights = '/home/kashefy/data/models/fcn_segm/fcn-32s-Pascal-context/fcn-32s-pascalcontext.caffemodel'
    net = caffe.Net(path_model, path_weights, caffe.TEST)
    # shape for input (data blob is N x C x H x W), set data
    net.blobs['data'].reshape(1, *in_.shape)
    net.blobs['data'].data[...] = in_    
Esempio n. 6
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 caffe.set_mode_cpu()
 
 # load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
 path_img = '/home/kashefy/data/VOCdevkit/VOC2012/JPEGImagesX/2008_000015.jpg'
 
 bgr_mean = np.array((104.00698793,116.66876762,122.67891434))
 im = Image.open(path_img)
 in_ = np.array(im, dtype=np.float32)
 in_ = in_[:,:,::-1]
 print in_.shape
 print in_
 in_ -= bgr_mean
 print in_
 in_ = in_.transpose((2,0,1))
 
 in_ = read_img_PIL(path_img, mean=bgr_mean)
 
 print 'in_'
 print in_[0, 0, 0:6]
 print in_[1, 0, 0:6]
 print in_[2, 0, 0:6]
 
 in2 = read_img_cv2(path_img, mean=bgr_mean)
 print in2.shape
 #in2[0, :, :] -= 104.00698793
 #in2[1, :, :] -= 116.66876762
 #in2[2, :, :] -= 122.67891434
 
 print in2[0, 0, 0:6]
 print in2[1, 0, 0:6]
 print in2[2, 0, 0:6]
Esempio n. 7
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    def test_read_img_PIL_shape(self):

        assert_equal(r.read_img_PIL(self.path_img1).shape, (3, 4, 2))
Esempio n. 8
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    def test_read_img_PIL_shape(self):

        assert_equal(r.read_img_PIL(self.path_img1).shape, (3, 4, 2))
Esempio n. 9
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def main(args):

    caffe.set_mode_cpu()

    # load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
    path_img = "/home/tairuichen/Documents/PASCAL-Context/VOC2010/JPEGImages/2007_000027.jpg"

    bgr_mean = np.array((104.00698793, 116.66876762, 122.67891434))
    im = Image.open(path_img)
    in_ = np.array(im, dtype=np.float32)
    in_ = in_[:, :, ::-1]
    print in_.shape
    print in_
    in_ -= bgr_mean
    print in_
    in_ = in_.transpose((2, 0, 1))

    in_ = read_img_PIL(path_img, mean=bgr_mean)

    print "in_"
    print in_[0, 0, 0:6]
    print in_[1, 0, 0:6]
    print in_[2, 0, 0:6]

    in2 = read_img_cv2(path_img, mean=bgr_mean)
    print in2.shape
    # in2[0, :, :] -= 104.00698793
    # in2[1, :, :] -= 116.66876762
    # in2[2, :, :] -= 122.67891434

    print in2[0, 0, 0:6]
    print in2[1, 0, 0:6]
    print in2[2, 0, 0:6]

    print np.all(in_ == in2)
    print in_[in_ != in2]
    print in2[in_ != in2]
    # return 0

    # load net
    path_model = "fcn-32s-Pascal-context/fcn-32s-pascal-deploy.prototxt"
    path_weights = "fcn-32s-Pascal-context/fcn-32s-pascal.caffemodel"
    net = caffe.Net(path_model, path_weights, caffe.TEST)
    # shape for input (data blob is N x C x H x W), set data
    net.blobs["data"].reshape(1, *in_.shape)
    net.blobs["data"].data[...] = in_

    # run net and take argmax for prediction
    net.forward()
    out = net.blobs["score"].data[0].argmax(axis=0)

    print "data after fwd"
    print net.blobs["data"].data[
        net.blobs["data"].data.shape[0] / 2 - 3 : net.blobs["data"].data.shape[0] / 2 + 3,
        net.blobs["data"].data.shape[1] / 2 - 3 : net.blobs["data"].data.shape[1] / 2 + 3,
    ]

    print "out"
    print out[out.shape[0] / 2 - 3 : out.shape[0] / 2 + 3, out.shape[1] / 2 - 3 : out.shape[1] / 2 + 3]
    plt.imshow(out)
    plt.show()