Python cv2.IMREAD_UNCHANGED Examples

The following are 30 code examples of cv2.IMREAD_UNCHANGED(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: util.py    From smashscan with MIT License 8 votes vote down vote up
def get_image_and_mask(img_location, gray_flag):

    # Load image from file with alpha channel (UNCHANGED flag). If an alpha
    # channel does not exist, just return the base image.
    img = cv2.imread(img_location, cv2.IMREAD_UNCHANGED)
    if img.shape[2] <= 3:
        return img, None

    # Create an alpha channel matrix  with values between 0-255. Then
    # threshold the alpha channel to create a binary mask.
    channels = cv2.split(img)
    mask = np.array(channels[3])
    _, mask = cv2.threshold(mask, 250, 255, cv2.THRESH_BINARY)

    # Convert image and mask to grayscale or BGR based on input flag.
    if gray_flag:
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
    else:
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
        mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)

    return img, mask


# Resize an image and mask based on an input scale ratio. 
Example #2
Source File: image_helper.py    From openseg.pytorch with MIT License 7 votes vote down vote up
def imfrombytes(content, flag='color'):
        """Read an image from bytes.

        Args:
            content (bytes): Image bytes got from files or other streams.
            flag (str): Same as :func:`imread`.

        Returns:
            ndarray: Loaded image array.
        """
        imread_flags = {
            'color': cv2.IMREAD_COLOR,
            'grayscale': cv2.IMREAD_GRAYSCALE,
            'unchanged': cv2.IMREAD_UNCHANGED
        }
        img_np = np.fromstring(content, np.uint8)
        flag = imread_flags[flag] if isinstance(flag, str) else flag
        img = cv2.imdecode(img_np, flag)
        return img 
Example #3
Source File: utils_image.py    From KAIR with MIT License 7 votes vote down vote up
def imread_uint(path, n_channels=3):
    #  input: path
    # output: HxWx3(RGB or GGG), or HxWx1 (G)
    if n_channels == 1:
        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
        img = np.expand_dims(img, axis=2)  # HxWx1
    elif n_channels == 3:
        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
        if img.ndim == 2:
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
        else:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
    return img


# --------------------------------------------
# matlab's imwrite
# -------------------------------------------- 
Example #4
Source File: cnn_use.py    From bonnet with GNU General Public License v3.0 6 votes vote down vote up
def predict_code(img, net, FLAGS):
  # predict feature map from image
  # open image
  cvim = cv2.imread(img, cv2.IMREAD_UNCHANGED)
  if cvim is None:
    print("No image to open for ", img)
    return
  # predict mask from image
  start = time.time()
  code = net.predict_code(cvim, path=FLAGS.path + '/' +
                          FLAGS.model, verbose=FLAGS.verbose)
  print("Prediction for img ", img, ". Elapsed: ", time.time() - start, "s")

  # reshape code to single dimension
  reshaped_code = np.reshape(code, (1, -1))
  # print("Shape", reshaped_code.shape)

  # save code to text file
  filename = FLAGS.log + "/" + \
      os.path.splitext(os.path.basename(img))[0] + ".txt"
  print("Saving feature map to: ", filename)
  np.savetxt(filename, reshaped_code, fmt="%.8f", delimiter=" ")

  return 
Example #5
Source File: make_tinyimagenet_c.py    From robustness with Apache License 2.0 6 votes vote down vote up
def motion_blur(x, severity=1):
    c = [(10,1), (10,1.5), (10,2), (10,2.5), (12,3)][severity - 1]

    output = BytesIO()
    x.save(output, format='PNG')
    x = MotionImage(blob=output.getvalue())

    x.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))

    x = cv2.imdecode(np.fromstring(x.make_blob(), np.uint8),
                     cv2.IMREAD_UNCHANGED)

    if x.shape != (64, 64):
        return np.clip(x[..., [2, 1, 0]], 0, 255)  # BGR to RGB
    else:  # greyscale to RGB
        return np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) 
Example #6
Source File: util.py    From BasicSR with Apache License 2.0 6 votes vote down vote up
def read_img(env, path, size=None):
    '''read image by cv2 or from lmdb
    return: Numpy float32, HWC, BGR, [0,1]'''
    if env is None:  # img
        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    else:
        img = _read_img_lmdb(env, path, size)
    img = img.astype(np.float32) / 255.
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    # some images have 4 channels
    if img.shape[2] > 3:
        img = img[:, :, :3]
    return img


####################
# image processing
# process on numpy image
#################### 
Example #7
Source File: tools.py    From keras-ocr with MIT License 6 votes vote down vote up
def read(filepath_or_buffer: typing.Union[str, io.BytesIO]):
    """Read a file into an image object

    Args:
        filepath_or_buffer: The path to the file, a URL, or any object
            with a `read` method (such as `io.BytesIO`)
    """
    if isinstance(filepath_or_buffer, np.ndarray):
        return filepath_or_buffer
    if hasattr(filepath_or_buffer, 'read'):
        image = np.asarray(bytearray(filepath_or_buffer.read()), dtype=np.uint8)
        image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED)
    elif isinstance(filepath_or_buffer, str):
        if validators.url(filepath_or_buffer):
            return read(urllib.request.urlopen(filepath_or_buffer))
        assert os.path.isfile(filepath_or_buffer), \
            'Could not find image at path: ' + filepath_or_buffer
        image = cv2.imread(filepath_or_buffer)
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) 
Example #8
Source File: make_imagenet_c.py    From robustness with Apache License 2.0 6 votes vote down vote up
def motion_blur(x, severity=1):
    c = [(10, 3), (15, 5), (15, 8), (15, 12), (20, 15)][severity - 1]

    output = BytesIO()
    x.save(output, format='PNG')
    x = MotionImage(blob=output.getvalue())

    x.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))

    x = cv2.imdecode(np.fromstring(x.make_blob(), np.uint8),
                     cv2.IMREAD_UNCHANGED)

    if x.shape != (224, 224):
        return np.clip(x[..., [2, 1, 0]], 0, 255)  # BGR to RGB
    else:  # greyscale to RGB
        return np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) 
Example #9
Source File: util.py    From IKC with Apache License 2.0 6 votes vote down vote up
def read_img(env, path, size=None):
    '''read image by cv2 or from lmdb
    return: Numpy float32, HWC, BGR, [0,1]'''
    if env is None:  # img
        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    else:
        img = _read_img_lmdb(env, path, size)
    img = img.astype(np.float32) / 255.
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    # some images have 4 channels
    if img.shape[2] > 3:
        img = img[:, :, :3]
    return img


####################
# image processing
# process on numpy image
#################### 
Example #10
Source File: corruptions.py    From robustness with Apache License 2.0 6 votes vote down vote up
def motion_blur(x, severity=1):
    c = [(10, 3), (15, 5), (15, 8), (15, 12), (20, 15)][severity - 1]

    output = BytesIO()
    x.save(output, format='PNG')
    x = MotionImage(blob=output.getvalue())

    x.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))

    x = cv2.imdecode(np.fromstring(x.make_blob(), np.uint8),
                     cv2.IMREAD_UNCHANGED)

    if x.shape != (224, 224):
        return np.clip(x[..., [2, 1, 0]], 0, 255)  # BGR to RGB
    else:  # greyscale to RGB
        return np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) 
Example #11
Source File: abstract_dataset.py    From bonnet with GNU General Public License v3.0 6 votes vote down vote up
def next_batch(self, size):
    '''
      Return size items (wraps around if the last elements are less than a
      batch size. Be careful with this for evaluation)
    '''
    # different if images are being buffered or not
    images = []
    labels = []
    names = []
    if self.buff:
      for i in range(0, size):
        images.append(self.img_q.get())  # blocking
        labels.append(self.lbl_q.get())  # blocking
        names.append(self.name_q.get())  # blocking
    else:
      for i in range(0, size):
        img = cv2.imread(self.images[self.idx], cv2.IMREAD_UNCHANGED)
        lbl = cv2.imread(self.labels[self.idx], 0)
        images.append(img)
        labels.append(lbl)
        names.append(os.path.basename(self.images[self.idx]))
        self.idx += 1
        if self.idx == self.num_examples:
          self.idx = 0
    return images, labels, names 
Example #12
Source File: utils.py    From pytorch-serverless with MIT License 6 votes vote down vote up
def open_image(path):
	""" Opens an image using OpenCV given the file path.
	:param path: the file path of the image
	:return: the image in RGB format as numpy array of floats normalized to range between 0.0 - 1.0
	"""
	flags = cv2.IMREAD_UNCHANGED+cv2.IMREAD_ANYDEPTH+cv2.IMREAD_ANYCOLOR
	path = str(path)
	if not os.path.exists(path):
		raise OSError(f'No such file or directory: {path}')
	elif os.path.isdir(path):
		raise OSError(f'Is a directory: {path}')
	else:
		try:
			im = cv2.imread(str(path), flags).astype(np.float32)/255
			if im is None: raise OSError(f'File not recognized by opencv: {path}')
			return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
		except Exception as e:
			raise OSError(f'Error handling image at: {path}') from e 
Example #13
Source File: util.py    From real-world-sr with MIT License 6 votes vote down vote up
def read_img(env, path, size=None):
    '''read image by cv2 or from lmdb
    return: Numpy float32, HWC, BGR, [0,1]'''
    if env is None:  # img
        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    else:
        img = _read_img_lmdb(env, path, size)
    img = img.astype(np.float32) / 255.
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    # some images have 4 channels
    if img.shape[2] > 3:
        img = img[:, :, :3]
    return img


####################
# image processing
# process on numpy image
#################### 
Example #14
Source File: make_imagenet_c_inception.py    From robustness with Apache License 2.0 6 votes vote down vote up
def motion_blur(x, severity=1):
    c = [(12,4), (17,6), (17, 9), (17,13), (22,16)][severity - 1]

    output = BytesIO()
    x.save(output, format='PNG')
    x = MotionImage(blob=output.getvalue())

    x.motion_blur(radius=c[0], sigma=c[1], angle=np.random.uniform(-45, 45))

    x = cv2.imdecode(np.fromstring(x.make_blob(), np.uint8),
                     cv2.IMREAD_UNCHANGED)

    if x.shape != (299, 299):
        return np.clip(x[..., [2, 1, 0]], 0, 255)  # BGR to RGB
    else:  # greyscale to RGB
        return np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) 
Example #15
Source File: data_loaders.py    From Pix2Vox with MIT License 6 votes vote down vote up
def get_datum(self, idx):
        taxonomy_name = self.file_list[idx]['taxonomy_name']
        sample_name = self.file_list[idx]['sample_name']
        rendering_image_path = self.file_list[idx]['rendering_image']
        bounding_box = self.file_list[idx]['bounding_box']
        volume_path = self.file_list[idx]['volume']

        # Get data of rendering images
        rendering_image = cv2.imread(rendering_image_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.

        if len(rendering_image.shape) < 3:
            print('[WARN] %s It seems the image file %s is grayscale.' % (dt.now(), rendering_image_path))
            rendering_image = np.stack((rendering_image, ) * 3, -1)

        # Get data of volume
        with open(volume_path, 'rb') as f:
            volume = utils.binvox_rw.read_as_3d_array(f)
            volume = volume.data.astype(np.float32)

        return taxonomy_name, sample_name, np.asarray([rendering_image]), volume, bounding_box


# //////////////////////////////// = End of Pascal3dDataset Class Definition = ///////////////////////////////// # 
Example #16
Source File: data_loaders.py    From Pix2Vox with MIT License 6 votes vote down vote up
def get_datum(self, idx):
        taxonomy_name = self.file_list[idx]['taxonomy_name']
        sample_name = self.file_list[idx]['sample_name']
        rendering_image_path = self.file_list[idx]['rendering_image']
        bounding_box = self.file_list[idx]['bounding_box']
        volume_path = self.file_list[idx]['volume']

        # Get data of rendering images
        rendering_image = cv2.imread(rendering_image_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.

        if len(rendering_image.shape) < 3:
            print('[WARN] %s It seems the image file %s is grayscale.' % (dt.now(), rendering_image_path))
            rendering_image = np.stack((rendering_image, ) * 3, -1)

        # Get data of volume
        with open(volume_path, 'rb') as f:
            volume = utils.binvox_rw.read_as_3d_array(f)
            volume = volume.data.astype(np.float32)

        return taxonomy_name, sample_name, np.asarray([rendering_image]), volume, bounding_box


# //////////////////////////////// = End of Pascal3dDataset Class Definition = ///////////////////////////////// # 
Example #17
Source File: cnn_use_pb.py    From bonnet with GNU General Public License v3.0 6 votes vote down vote up
def predict_mask(img, sess, input, output, FLAGS, DATA):
  # open image
  cvim = cv2.imread(img, cv2.IMREAD_UNCHANGED)
  if cvim is None:
    print("No image to open for ", img)
    return
  # predict mask from image
  start = time.time()
  mask = sess.run(output, feed_dict={input: [cvim]})
  print("Prediction for img ", img, ". Elapsed: ", time.time() - start, "s")
  # change to color
  color_mask = util.prediction_to_color(
      mask[0, :, :], DATA["label_remap"], DATA["color_map"])

  cv2.imwrite(FLAGS.log + "/" + os.path.basename(img), color_mask)

  if FLAGS.verbose:
    # show me the image
    # first, mix with image
    im, transparent_mask = util.transparency(cvim, color_mask)
    all_img = np.concatenate((im, transparent_mask, color_mask), axis=1)
    util.im_tight_plt(all_img)
    util.im_block()

  return 
Example #18
Source File: cnn_use_pb.py    From bonnet with GNU General Public License v3.0 6 votes vote down vote up
def predict_code(img, sess, input, output, FLAGS):
  # predict feature map from image
  # open image
  cvim = cv2.imread(img, cv2.IMREAD_UNCHANGED)
  if cvim is None:
    print("No image to open for ", img)
    return

  # predict code from image
  print("Prediction for img ", img)
  code = sess.run(output, feed_dict={input: [cvim]})

  # reshape code to single dimension
  reshaped_code = np.reshape(code, (1, -1))
  print("Shape", reshaped_code.shape)

  # save code to text file
  filename = FLAGS.log + "/" + \
      os.path.splitext(os.path.basename(img))[0] + ".txt"
  print("Saving feature map to: ", filename)
  np.savetxt(filename, reshaped_code, fmt="%.8f", delimiter=" ")

  return 
Example #19
Source File: cnn_use.py    From bonnet with GNU General Public License v3.0 5 votes vote down vote up
def predict_mask(img, net, FLAGS, DATA):
  # open image
  cvim = cv2.imread(img, cv2.IMREAD_UNCHANGED)
  if cvim is None:
    print("No image to open for ", img)
    return
  # predict mask from image
  start = time.time()
  mask = net.predict(cvim, path=FLAGS.path + '/' +
                     FLAGS.model, verbose=FLAGS.verbose)
  print("Prediction for img ", img, ". Elapsed: ", time.time() - start, "s")
  # change to color
  color_mask = util.prediction_to_color(
      mask, DATA["label_remap"], DATA["color_map"])

  # assess accuracy (if wanted)
  if FLAGS.label is not None:
    label = cv2.imread(FLAGS.label, 0)
    if label is None:
      print("No label to open")
      quit()
    net.individual_accuracy(mask, label)

  cv2.imwrite(FLAGS.log + "/" + os.path.basename(img), color_mask)

  if FLAGS.verbose:
    # show me the image
    # first, mix with image
    im, transparent_mask = util.transparency(cvim, color_mask)
    all_img = np.concatenate((im, transparent_mask, color_mask), axis=1)
    util.im_tight_plt(all_img)
    util.im_block()

  return 
Example #20
Source File: text_list_adapter.py    From lffd-pytorch with MIT License 5 votes vote down vote up
def get_one(self):
        """
        This function use 'yield' to return samples
        """
        while self.line_counter < len(self.lines):

            line = self.lines[self.line_counter].strip('\n').split(',')
            if line[1] == '1':  # pos sample
                assert len(line[3:]) == 4 * int(line[2])

            im = cv2.imread(line[0], cv2.IMREAD_UNCHANGED)

            if line[1] == '0':
                yield im, '0'
                self.line_counter += 1
                continue

            num_bboxes = int(line[2])
            bboxes = []
            for i in range(num_bboxes):
                x = float(line[3 + i * 4])
                y = float(line[3 + i * 4 + 1])
                width = float(line[3 + i * 4 + 2])
                height = float(line[3 + i * 4 + 3])

                bboxes.append([x, y, width, height])

            bboxes = numpy.array(bboxes, dtype=numpy.float32)
            yield im, bboxes

            self.line_counter += 1 
Example #21
Source File: text_list_adapter.py    From lffd-pytorch with MIT License 5 votes vote down vote up
def get_one(self):
        """
        This function use 'yield' to return samples
        """
        while self.line_counter < len(self.lines):

            line = self.lines[self.line_counter].strip('\n').split(',')
            if line[1] == '1':  # 如果是正样本,需要校验bbox的个数是否一样
                assert len(line[3:]) == 4 * int(line[2])

            im = cv2.imread(line[0], cv2.IMREAD_UNCHANGED)

            if line[1] == '0':
                yield im, '0'
                self.line_counter += 1
                continue

            num_bboxes = int(line[2])
            bboxes = []
            for i in range(num_bboxes):
                x = float(line[3 + i * 4])
                y = float(line[3 + i * 4 + 1])
                width = float(line[3 + i * 4 + 2])
                height = float(line[3 + i * 4 + 3])

                bboxes.append([x, y, width, height])

            bboxes = numpy.array(bboxes, dtype=numpy.float32)
            yield im, bboxes

            self.line_counter += 1 
Example #22
Source File: utils.py    From QuickDraw with MIT License 5 votes vote down vote up
def get_images(path, classes):
    images = [cv2.imread("{}/{}.png".format(path, item), cv2.IMREAD_UNCHANGED) for item in classes]
    return images 
Example #23
Source File: imgio_test.py    From SickZil-Machine with GNU Affero General Public License v3.0 5 votes vote down vote up
def test_load():
    path = './fixture/not_proj_dir/bgr1_mask.png'
    expected_qimg = QImage(path)
    expected_ndarr= cv2.imread(path, cv2.IMREAD_UNCHANGED)
    assert io.load(path) == expected_qimg
    assert np.array_equal(io.load(path,io.NDARR),expected_ndarr) 
Example #24
Source File: color2gray.py    From IKC with Apache License 2.0 5 votes vote down vote up
def worker(path, save_folder, mode, compression_level):
    img_name = os.path.basename(path)
    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR
    if mode == 'gray':
        img_y = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    else:
        img_y = bgr2ycbcr(img, only_y=True)
    cv2.imwrite(os.path.join(save_folder, img_name), img_y,
                [cv2.IMWRITE_PNG_COMPRESSION, compression_level])
    return 'Processing {:s} ...'.format(img_name) 
Example #25
Source File: extract_subimgs_single.py    From IKC with Apache License 2.0 5 votes vote down vote up
def worker(path, save_folder, crop_sz, step, thres_sz, compression_level):
    img_name = os.path.basename(path)
    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)

    n_channels = len(img.shape)
    if n_channels == 2:
        h, w = img.shape
    elif n_channels == 3:
        h, w, c = img.shape
    else:
        raise ValueError('Wrong image shape - {}'.format(n_channels))

    h_space = np.arange(0, h - crop_sz + 1, step)
    if h - (h_space[-1] + crop_sz) > thres_sz:
        h_space = np.append(h_space, h - crop_sz)
    w_space = np.arange(0, w - crop_sz + 1, step)
    if w - (w_space[-1] + crop_sz) > thres_sz:
        w_space = np.append(w_space, w - crop_sz)

    index = 0
    for x in h_space:
        for y in w_space:
            index += 1
            if n_channels == 2:
                crop_img = img[x:x + crop_sz, y:y + crop_sz]
            else:
                crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
            crop_img = np.ascontiguousarray(crop_img)
            # var = np.var(crop_img / 255)
            # if var > 0.008:
            #     print(img_name, index_str, var)
            cv2.imwrite(
                os.path.join(save_folder, img_name.replace('.png', '_s{:03d}.png'.format(index))),
                crop_img, [cv2.IMWRITE_PNG_COMPRESSION, compression_level])
    return 'Processing {:s} ...'.format(img_name) 
Example #26
Source File: cv.py    From droidbot with MIT License 5 votes vote down vote up
def load_image_from_buf(img_bytes):
    """
    Load an image from a byte array
    :param img_bytes: The byte array of an image
    :return:
    """
    import cv2
    import numpy
    img_bytes = numpy.array(img_bytes)
    return cv2.imdecode(img_bytes, cv2.IMREAD_UNCHANGED) 
Example #27
Source File: dataset.py    From deep-photometric-stereo-network with MIT License 5 votes vote down vote up
def load_data(self, root_path):
        def_png_path = os.path.join(root_path, "{light_index}.png")

        m, n = self.img_size
        M = np.zeros(shape=(m * n, self.light_num, 3), dtype=np.float32)
        for l in range(self.light_num):
            m_img = cv2.imread(def_png_path.format(light_index=l), cv2.IMREAD_UNCHANGED)[:, :, ::-1]
            # m_img = cv2.imread(def_png_path.format(light_index=l))[:, :, ::-1]
            M[:, l, :] = m_img.reshape(-1, 3)

        obj_name, brdf_name = self.data_path2name(root_path + "/")
        N, m, n, mask = self.__load_normal_png(os.path.join(self.dataset_path, obj_name, "{}.png".format(obj_name)))

        return M, N, mask 
Example #28
Source File: codecs.py    From petastorm with Apache License 2.0 5 votes vote down vote up
def decode(self, unischema_field, value):
        """Decodes the image using OpenCV."""

        # cv returns a BGR or grayscale image. Convert to RGB (unless a grayscale image).
        image_bgr_or_gray = cv2.imdecode(np.frombuffer(value, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
        if len(image_bgr_or_gray.shape) == 2:
            # Greyscale image
            return image_bgr_or_gray
        elif len(image_bgr_or_gray.shape) == 3 and image_bgr_or_gray.shape[2] == 3:
            # Convert BGR to RGB (opencv assumes BGR)
            image_rgb = image_bgr_or_gray[:, :, (2, 1, 0)]
            return image_rgb
        else:
            raise ValueError('Unexpected image dimensions. Supported dimensions are (H, W) or (H, W, 3). '
                             'Got {}'.format(image_bgr_or_gray.shape)) 
Example #29
Source File: test_transform_set_select_curate_flask_app.py    From deepstar with BSD 3-Clause Clear License 5 votes vote down vote up
def test_transform_get(self):
        with deepstar_path():
            self.setUp_()

            api = TransformSetSelectCurateFlaskApp(1).api.app.test_client()

            response = api.get('/transform_sets/1/transforms/1')
            self.assertEqual(response.status_code, 200)
            self.assertIsInstance(cv2.imdecode(np.fromstring(response.get_data(), dtype=np.uint8), cv2.IMREAD_UNCHANGED), np.ndarray)  # noqa 
Example #30
Source File: test_transform_set_select_curate_flask_app.py    From deepstar with BSD 3-Clause Clear License 5 votes vote down vote up
def test_static(self):
        with deepstar_path():
            self.setUp_()

            api = TransformSetSelectCurateFlaskApp(1).api.app.test_client()

            response = api.get('/static/img/checkmark.png')
            self.assertEqual(response.status_code, 200)
            self.assertIsInstance(cv2.imdecode(np.fromstring(response.get_data(), dtype=np.uint8), cv2.IMREAD_UNCHANGED), np.ndarray)  # noqa

            response = api.get('/static/img/xmark.png')
            self.assertEqual(response.status_code, 200)
            self.assertIsInstance(cv2.imdecode(np.fromstring(response.get_data(), dtype=np.uint8), cv2.IMREAD_UNCHANGED), np.ndarray)  # noqa