Loading deepchem/trans/tests/test_transformers.py +39 −0 Original line number Diff line number Diff line Loading @@ -587,6 +587,35 @@ class TestTransformers(unittest.TestCase): check_blur = scipy.ndimage.gaussian_filter(self.d, 1.5) assert np.allclose(check_blur, blurred) def test_center_crop(self): # Check center crop dt = DataTransforms(self.d) x_crop = 50 y_crop = 50 crop = dt.center_crop(x_crop, y_crop) y = self.d.shape[0] x = self.d.shape[1] x_start = x // 2 - (x_crop // 2) y_start = y // 2 - (y_crop // 2) check_crop = self.d[y_start:y_start + y_crop, x_start:x_start + x_crop] assert np.allclose(check_crop, crop) def test_crop(self): #Check crop dt = DataTransforms(self.d) crop = dt.crop(0, 10, 0, 10) y = self.d.shape[0] x = self.d.shape[1] check_crop = self.d[10:y - 10, 0:x - 0] assert np.allclose(crop, check_crop) def test_convert2gray(self): # Check convert2gray dt = DataTransforms(self.d) gray = dt.convert2gray() check_gray = np.dot(self.d[..., :3], [0.2989, 0.5870, 0.1140]) assert np.allclose(check_gray, gray) def test_rotation(self): # Check rotation dt = DataTransforms(self.d) Loading Loading @@ -677,3 +706,13 @@ class TestTransformers(unittest.TestCase): # atoms. These are denoted the "parents" for idm, mol in enumerate(dataset.X): assert dataset.X[idm].get_num_atoms() == len(dataset.X[idm].parents) def test_median_filter(self): #Check median filter from PIL import Image, ImageFilter dt = DataTransforms(self.d) filtered = dt.median_filter(size=3) image = Image.fromarray(self.d) image = image.filter(ImageFilter.MedianFilter(size=3)) check_filtered = np.array(image) assert np.allclose(check_filtered, filtered) deepchem/trans/transformers.py +79 −2 Original line number Diff line number Diff line Loading @@ -1306,8 +1306,15 @@ class DataTransforms(Transformer): def rotate(self, angle=0): """ Rotates the image Parameters: angle (default = 0 i.e no rotation) - Denotes angle by which the image should be rotated (in Degrees) Parameters ---------- angle: float (default = 0 i.e no rotation) Denotes angle by which the image should be rotated (in Degrees) Returns ---------- The rotated imput array """ return scipy.ndimage.rotate(self.Image, angle) Loading @@ -1318,6 +1325,59 @@ class DataTransforms(Transformer): """ return scipy.ndimage.gaussian_filter(self.Image, sigma) def center_crop(self, x_crop, y_crop): """ Crops the image from the center Parameters ---------- x_crop: int the total number of pixels to remove in the horizontal direction, evenly split between the left and right sides y_crop: int the total number of pixels to remove in the vertical direction, evenly split between the top and bottom sides Returns ---------- The center cropped input array """ y = self.Image.shape[0] x = self.Image.shape[1] x_start = x // 2 - (x_crop // 2) y_start = y // 2 - (y_crop // 2) return self.Image[y_start:y_start + y_crop, x_start:x_start + x_crop] def crop(self, left, top, right, bottom): """ Crops the image and returns the specified rectangular region from an image Parameters ---------- left: int the number of pixels to exclude from the left of the image top: int the number of pixels to exclude from the top of the image right: int the number of pixels to exclude from the right of the image bottom: int the number of pixels to exclude from the bottom of the image Returns ---------- The cropped input array """ y = self.Image.shape[0] x = self.Image.shape[1] return self.Image[top:y - bottom, left:x - right] def convert2gray(self): """ Converts the image to grayscale. The coefficients correspond to the Y' component of the Y'UV color system. Returns ---------- The grayscale image. """ return np.dot(self.Image[..., :3], [0.2989, 0.5870, 0.1140]) def shift(self, width, height, mode='constant', order=3): """Shifts the image Parameters: Loading Loading @@ -1358,3 +1418,20 @@ class DataTransforms(Transformer): x[noise < (prob / 2)] = pepper x[noise > (1 - prob / 2)] = salt return x def median_filter(self, size): """ Calculates a multidimensional median filter Parameters ---------- size: int The kernel size in pixels. Returns ---------- The median filtered image. """ from PIL import Image, ImageFilter image = Image.fromarray(self.Image) image = image.filter(ImageFilter.MedianFilter(size=size)) return np.array(image) Loading
deepchem/trans/tests/test_transformers.py +39 −0 Original line number Diff line number Diff line Loading @@ -587,6 +587,35 @@ class TestTransformers(unittest.TestCase): check_blur = scipy.ndimage.gaussian_filter(self.d, 1.5) assert np.allclose(check_blur, blurred) def test_center_crop(self): # Check center crop dt = DataTransforms(self.d) x_crop = 50 y_crop = 50 crop = dt.center_crop(x_crop, y_crop) y = self.d.shape[0] x = self.d.shape[1] x_start = x // 2 - (x_crop // 2) y_start = y // 2 - (y_crop // 2) check_crop = self.d[y_start:y_start + y_crop, x_start:x_start + x_crop] assert np.allclose(check_crop, crop) def test_crop(self): #Check crop dt = DataTransforms(self.d) crop = dt.crop(0, 10, 0, 10) y = self.d.shape[0] x = self.d.shape[1] check_crop = self.d[10:y - 10, 0:x - 0] assert np.allclose(crop, check_crop) def test_convert2gray(self): # Check convert2gray dt = DataTransforms(self.d) gray = dt.convert2gray() check_gray = np.dot(self.d[..., :3], [0.2989, 0.5870, 0.1140]) assert np.allclose(check_gray, gray) def test_rotation(self): # Check rotation dt = DataTransforms(self.d) Loading Loading @@ -677,3 +706,13 @@ class TestTransformers(unittest.TestCase): # atoms. These are denoted the "parents" for idm, mol in enumerate(dataset.X): assert dataset.X[idm].get_num_atoms() == len(dataset.X[idm].parents) def test_median_filter(self): #Check median filter from PIL import Image, ImageFilter dt = DataTransforms(self.d) filtered = dt.median_filter(size=3) image = Image.fromarray(self.d) image = image.filter(ImageFilter.MedianFilter(size=3)) check_filtered = np.array(image) assert np.allclose(check_filtered, filtered)
deepchem/trans/transformers.py +79 −2 Original line number Diff line number Diff line Loading @@ -1306,8 +1306,15 @@ class DataTransforms(Transformer): def rotate(self, angle=0): """ Rotates the image Parameters: angle (default = 0 i.e no rotation) - Denotes angle by which the image should be rotated (in Degrees) Parameters ---------- angle: float (default = 0 i.e no rotation) Denotes angle by which the image should be rotated (in Degrees) Returns ---------- The rotated imput array """ return scipy.ndimage.rotate(self.Image, angle) Loading @@ -1318,6 +1325,59 @@ class DataTransforms(Transformer): """ return scipy.ndimage.gaussian_filter(self.Image, sigma) def center_crop(self, x_crop, y_crop): """ Crops the image from the center Parameters ---------- x_crop: int the total number of pixels to remove in the horizontal direction, evenly split between the left and right sides y_crop: int the total number of pixels to remove in the vertical direction, evenly split between the top and bottom sides Returns ---------- The center cropped input array """ y = self.Image.shape[0] x = self.Image.shape[1] x_start = x // 2 - (x_crop // 2) y_start = y // 2 - (y_crop // 2) return self.Image[y_start:y_start + y_crop, x_start:x_start + x_crop] def crop(self, left, top, right, bottom): """ Crops the image and returns the specified rectangular region from an image Parameters ---------- left: int the number of pixels to exclude from the left of the image top: int the number of pixels to exclude from the top of the image right: int the number of pixels to exclude from the right of the image bottom: int the number of pixels to exclude from the bottom of the image Returns ---------- The cropped input array """ y = self.Image.shape[0] x = self.Image.shape[1] return self.Image[top:y - bottom, left:x - right] def convert2gray(self): """ Converts the image to grayscale. The coefficients correspond to the Y' component of the Y'UV color system. Returns ---------- The grayscale image. """ return np.dot(self.Image[..., :3], [0.2989, 0.5870, 0.1140]) def shift(self, width, height, mode='constant', order=3): """Shifts the image Parameters: Loading Loading @@ -1358,3 +1418,20 @@ class DataTransforms(Transformer): x[noise < (prob / 2)] = pepper x[noise > (1 - prob / 2)] = salt return x def median_filter(self, size): """ Calculates a multidimensional median filter Parameters ---------- size: int The kernel size in pixels. Returns ---------- The median filtered image. """ from PIL import Image, ImageFilter image = Image.fromarray(self.Image) image = image.filter(ImageFilter.MedianFilter(size=size)) return np.array(image)
