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How to use compare_image_files method in toolium

Best Python code snippet using toolium_python

test_analysis.py

Source:test_analysis.py

...5from nlweb.image_utils import (6    download_images, download_image, resample_images7)8from .nv_test_data import TARGET_DATA_IMG_IDS9def compare_image_files(img_a, img_b):10    assert img_a.get_data().shape == img_b.get_data().shape11    assert (img_a.get_data() == img_b.get_data()).all()12def test_train_model(tmpdir):13    algorithm = 'ridge'14    output_dir = str(tmpdir)15    cache = FileCache('cache')16    client = HTTPClient(cache)17    image_list = download_images(client, TARGET_DATA_IMG_IDS,18                                 output_dir)19    image_list = resample_images(cache, image_list, output_dir)20    cv = {'type': 'kfolds', 'n_folds': 10}21    # cv = {'type': 'loso'}22    result = analysis.train_model(image_list, algorithm, cv, output_dir)23    filename = '%s_weightmap.nii.gz' % algorithm24    assert result['weightmap'] == filename25    sample_img = nib.load(os.path.join(os.path.dirname(__file__), filename))26    result_img = nib.load(os.path.join(output_dir, filename))27    compare_image_files(sample_img, result_img)28def test_masked_train_model(tmpdir):29    algorithm = 'ridge'30    mask_image_id = 1865031    output_dir = str(tmpdir)32    cache = FileCache('cache')33    client = HTTPClient(cache)34    image_list = download_images(client, TARGET_DATA_IMG_IDS,35                                 output_dir)36    cv = {'type': 'kfolds', 'n_folds': 10}37    mask_filepath = download_image(client, mask_image_id, output_dir)38    result = analysis.train_model(39        image_list, algorithm, cv, output_dir,40        file_path_key='original_file',41        mask=mask_filepath)42    filename = '%s_weightmap.nii.gz' % algorithm43    assert result['weightmap'] == filename44    sample_img = nib.load(os.path.join(os.path.dirname(__file__), filename))45    result_img = nib.load(os.path.join(output_dir, filename))46    compare_image_files(sample_img, result_img)47def test_model_test(tmpdir):48    cache = FileCache('cache')49    client = HTTPClient(cache)50    output_dir = str(tmpdir)51    weight_map_filename = os.path.join(os.path.dirname(__file__),52                                       'ridge_weightmap.nii.gz')53    image_list = download_images(client, [TARGET_DATA_IMG_IDS[0],54                                          TARGET_DATA_IMG_IDS[30],55                                          TARGET_DATA_IMG_IDS[62]],56                                 output_dir)57    # Add dummy name and thumbnail58    image_list = [dict(thumbnail='image.png',59                       name='name %s' % item['id'],60                       **item) for item in image_list]...

ImageCompareTest.py

Source:ImageCompareTest.py

...54        im2 = Image.new('RGB', (256, 256), 'black')55        im1.save(self.imfile1)56        im2.save(self.imfile2)57        img_comp = ImageTools()58        img_diff = img_comp.compare_image_files(self.imfile1, self.imfile2)59        self.assertEqual(img_diff, 100.0)60    61    def test_imagefiles_calling_histogram_algo(self):62        im1 = Image.new('RGB', (256, 256), 'white')63        im2 = Image.new('RGB', (256, 256), 'black')64        im1.save(self.imfile1)65        im2.save(self.imfile2)66        img_comp = ImageTools()67        img_diff = img_comp.compare_image_files(self.imfile1, self.imfile2, algorithm='histogram')68        self.assertGreater(img_diff, 100.0)69    70    def test_imagefiles_calling_unknown_algo_returns_error(self):71        im1 = Image.new('RGB', (256, 256), 'white')72        im2 = Image.new('RGB', (256, 256), 'black')73        im1.save(self.imfile1)74        im2.save(self.imfile2)75        img_comp = ImageTools()76        img_diff = img_comp.compare_image_files(self.imfile1, self.imfile2, algorithm='unknown')77        self.assertEqual(img_diff, 'Unsupported algorithm')78    def test_nonexist_imagefiles_return_error(self):79        im1 = Image.new('RGB', (256, 256), 'white')80        im1.save(self.imfile1)81        img_comp = ImageTools()82        img_diff = img_comp.compare_image_files(self.imfile1, 'notreal.jpg')83        self.assertEqual(img_diff, 'Error reading one or more files')84if __name__ == '__main__':...

compare_image.py

Source:compare_image.py

...14    def __init__(self):15        pass16        17    18    def compare_image_files(self, file1, file2):19        img1 = imread(file1)20        img2 = imread(file2)21        return self.compare_images(img1, img2)22    def compare_images(self, img1, img2):23        # normalize to compensate for exposure difference, this may be unnecessary24        # consider disabling it25        img1 = self.normalize(img1)26        img2 = self.normalize(img2)27        # calculate the difference and its norms28        diff = img1 - img2  # elementwise for scipy arrays29        m_norm = np.sum(abs(diff))/img1.size  # Manhattan norm30        z_norm = norm(diff.ravel(), 0)/img1.size  # Zero norm31        # print ("Manhattan norm:", m_norm, "/ per pixel:", m_norm/img1.size)32        # print ("Zero norm:", z_norm, "/ per pixel:", z_norm*1.0/img1.size)33    34        return (m_norm, z_norm)35    def to_grayscale(self, arr):36        "If arr is a color image (3D array), convert it to grayscale (2D array)."37        if len(arr.shape) == 3:38            return average(arr, -1)  # average over the last axis (color channels)39        else:40            return arr41        42    def normalize(self, arr):43        rng = arr.max()-arr.min()44        amin = arr.min()45        return (arr-amin)*255/rng46def main():47    img1 = imread("live0.png")48    img2 = imread("live200.png")49    img = imagecompare()50    n_m, n_0 = img.compare_image_files("live0.png", "live200.png")51    print ("Manhattan norm:", n_m, "/ per pixel:", n_m/img1.size)52    print ("Zero norm:", n_0, "/ per pixel:", n_0*1.0/img1.size)53    54if __name__ == '__main__':...

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