Python cv2.INTER_CUBIC Examples

def format_img(img, C):
	img_min_side = float(C.im_size)
	(height,width,_) = img.shape
	
	if width <= height:
		f = img_min_side/width
		new_height = int(f * height)
		new_width = int(img_min_side)
	else:
		f = img_min_side/height
		new_width = int(f * width)
		new_height = int(img_min_side)
	fx = width/float(new_width)
	fy = height/float(new_height)
	img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
	img = img[:, :, (2, 1, 0)]
	img = img.astype(np.float32)
	img[:, :, 0] -= C.img_channel_mean[0]
	img[:, :, 1] -= C.img_channel_mean[1]
	img[:, :, 2] -= C.img_channel_mean[2]
	img /= C.img_scaling_factor
	img = np.transpose(img, (2, 0, 1))
	img = np.expand_dims(img, axis=0)
	return img, fx, fy 

def format_img(img, C):
	img_min_side = float(C.im_size)
	(height,width,_) = img.shape
	
	if width <= height:
		f = img_min_side/width
		new_height = int(f * height)
		new_width = int(img_min_side)
	else:
		f = img_min_side/height
		new_width = int(f * width)
		new_height = int(img_min_side)
	img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
	img = img[:, :, (2, 1, 0)]
	img = img.astype(np.float32)
	img[:, :, 0] -= C.img_channel_mean[0]
	img[:, :, 1] -= C.img_channel_mean[1]
	img[:, :, 2] -= C.img_channel_mean[2]
	img /= C.img_scaling_factor
	img = np.transpose(img, (2, 0, 1))
	img = np.expand_dims(img, axis=0)
	return img 

def __call__(self, sample):

        # Fixed range of scales
        sc = self.scales[random.randint(0, len(self.scales) - 1)]

        for elem in sample.keys():
            if 'fname' in elem:
                continue
            tmp = sample[elem]

            if tmp.ndim == 2:
                flagval = cv2.INTER_NEAREST
            else:
                flagval = cv2.INTER_CUBIC

            tmp = cv2.resize(tmp, None, fx=sc, fy=sc, interpolation=flagval)

            sample[elem] = tmp

        return sample 

def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):
    """Crop the given numpy ndarray and resize it to desired size.
    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
    Args:
        img (numpy ndarray): Image to be cropped.
        i: Upper pixel coordinate.
        j: Left pixel coordinate.
        h: Height of the cropped image.
        w: Width of the cropped image.
        size (sequence or int): Desired output size. Same semantics as ``scale``.
        interpolation (int, optional): Desired interpolation. Default is
            ``cv2.INTER_CUBIC``.
    Returns:
        PIL Image: Cropped image.
    """
    assert _is_numpy_image(img), 'img should be numpy image'
    img = crop(img, i, j, h, w)
    img = resize(img, size, interpolation=interpolation)
    return img 

def get_data(name, batch):
    isTrain = name == 'train'
    image_shape = 224

    if isTrain:
        augmentors = [
            # use lighter augs if model is too small
            GoogleNetResize(crop_area_fraction=0.49 if args.width_ratio < 1 else 0.08,
                           target_shape=image_shape),
            imgaug.RandomOrderAug(
                [imgaug.BrightnessScale((0.6, 1.4), clip=False),
                 imgaug.Contrast((0.6, 1.4), clip=False),
                 imgaug.Saturation(0.4, rgb=False),
                ]),
            imgaug.Flip(horiz=True),
        ]
    else:
        augmentors = [
            imgaug.ResizeShortestEdge(int(image_shape*256/224), cv2.INTER_CUBIC),
            imgaug.CenterCrop((image_shape, image_shape)),
        ]
    return get_imagenet_dataflow(args.data_dir, name, batch, augmentors, 
                       meta_dir = args.meta_dir) 

def _augment(self, img, _):
        h, w = img.shape[:2]
        area = h * w
        for _ in range(10):
            targetArea = self.rng.uniform(self.crop_area_fraction, 1.0) * area
            aspectR = self.rng.uniform(self.aspect_ratio_low, self.aspect_ratio_high)
            ww = int(np.sqrt(targetArea * aspectR) + 0.5)
            hh = int(np.sqrt(targetArea / aspectR) + 0.5)
            if self.rng.uniform() < 0.5:
                ww, hh = hh, ww
            if hh <= h and ww <= w:
                x1 = 0 if w == ww else self.rng.randint(0, w - ww)
                y1 = 0 if h == hh else self.rng.randint(0, h - hh)
                out = img[y1:y1 + hh, x1:x1 + ww]
                out = cv2.resize(out, (self.target_shape, self.target_shape), interpolation=cv2.INTER_CUBIC)
                return out
        out = imgaug.ResizeShortestEdge(self.target_shape, interp=cv2.INTER_CUBIC).augment(img)
        out = imgaug.CenterCrop(self.target_shape).augment(out)
        return out 

def _resize_image(img):
    dst_width = CFG.ARCH.INPUT_SIZE[0]
    dst_height = CFG.ARCH.INPUT_SIZE[1]
    h_old, w_old, _ = img.shape
    height = dst_height
    width = int(w_old * height / h_old)
    if width < dst_width:
        left_padding = int((dst_width - width)/2)
        right_padding = dst_width - width - left_padding
        resized_img = cv2.resize(img, (width, height), interpolation=cv2.INTER_CUBIC)
        resized_img = cv2.copyMakeBorder(resized_img, 0, 0, left_padding, right_padding,
            cv2.BORDER_CONSTANT, value=[255, 255, 255])
    else:
        resized_img = cv2.resize(img, (dst_width, height), interpolation=cv2.INTER_CUBIC)

    return resized_img 

def fixed_resize(sample, resolution, flagval=None):

    if flagval is None:
        if ((sample == 0) | (sample == 1)).all():
            flagval = cv2.INTER_NEAREST
        else:
            flagval = cv2.INTER_CUBIC

    if isinstance(resolution, int):
        tmp = [resolution, resolution]
        tmp[np.argmax(sample.shape[:2])] = int(round(float(resolution)/np.min(sample.shape[:2])*np.max(sample.shape[:2])))
        resolution = tuple(tmp)

    if sample.ndim == 2 or (sample.ndim == 3 and sample.shape[2] == 3):
        sample = cv2.resize(sample, resolution[::-1], interpolation=flagval)
    else:
        tmp = sample
        sample = np.zeros(np.append(resolution, tmp.shape[2]), dtype=np.float32)
        for ii in range(sample.shape[2]):
            sample[:, :, ii] = cv2.resize(tmp[:, :, ii], resolution[::-1], interpolation=flagval)
    return sample 

def get_mnist_data(is_train, image_size, batchsize):
    ds = MNISTCh('train' if is_train else 'test', shuffle=True)

    if is_train:
        augs = [
            imgaug.RandomApplyAug(imgaug.RandomResize((0.8, 1.2), (0.8, 1.2)), 0.3),
            imgaug.RandomApplyAug(imgaug.RotationAndCropValid(15), 0.5),
            imgaug.RandomApplyAug(imgaug.SaltPepperNoise(white_prob=0.01, black_prob=0.01), 0.25),
            imgaug.Resize((224, 224), cv2.INTER_AREA)
        ]
        ds = AugmentImageComponent(ds, augs)
        ds = PrefetchData(ds, 128*10, multiprocessing.cpu_count())
        ds = BatchData(ds, batchsize)
        ds = PrefetchData(ds, 256, 4)
    else:
        # no augmentation, only resizing
        augs = [
            imgaug.Resize((image_size, image_size), cv2.INTER_CUBIC),
        ]
        ds = AugmentImageComponent(ds, augs)
        ds = BatchData(ds, batchsize)
        ds = PrefetchData(ds, 20, 2)
    return ds 

def update_vis(self):
        ims = self.opt_engine.get_images(self.frame_id)

        if ims is not None:
            self.ims = ims

        if self.ims is None:
            return

        ims_show = []
        n_imgs = self.ims.shape[0]
        for n in range(n_imgs):
            # im = ims[n]
            im_s = cv2.resize(self.ims[n], (self.width, self.width), interpolation=cv2.INTER_CUBIC)
            if n == self.select_id and self.topK > 1:
                t = 3  # thickness
                cv2.rectangle(im_s, (t, t), (self.width - t, self.width - t), (0, 255, 0), t)
            im_s = im_s[np.newaxis, ...]
            ims_show.append(im_s)
        if ims_show:
            ims_show = np.concatenate(ims_show, axis=0)
            g_tmp = utils.grid_vis(ims_show, self.grid_size[1], self.grid_size[0]) # (nh, nw)
            self.vis_results = g_tmp.copy()
            self.update() 

def format_img(self, img, box_2d):

        # Should this happen? or does normalize take care of it. YOLO doesnt like
        # img=img.astype(np.float) / 255

        # torch transforms
        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                std=[0.229, 0.224, 0.225])
        process = transforms.Compose ([
            transforms.ToTensor(),
            normalize
        ])

        # crop image
        pt1 = box_2d[0]
        pt2 = box_2d[1]
        crop = img[pt1[1]:pt2[1]+1, pt1[0]:pt2[0]+1]
        crop = cv2.resize(src = crop, dsize=(224, 224), interpolation=cv2.INTER_CUBIC)

        # recolor, reformat
        batch = process(crop)

        return batch 

def decode_pose(img_orig, heatmaps, pafs):
    param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}

    # Bottom-up approach:
    # Step 1: find all joints in the image (organized by joint type: [0]=nose, [1]=neck...)
    joint_list_per_joint_type = NMS(param, heatmaps, img_orig.shape[0] / float(heatmaps.shape[0]))
    # joint_list is an unravel'd version of joint_list_per_joint, where we add
    # a 5th column to indicate the joint_type (0=nose, 1=neck...)
    joint_list = np.array([tuple(peak) + (joint_type,) for joint_type, joint_peaks in enumerate(joint_list_per_joint_type) for peak in joint_peaks])

    # Step 2: find which joints go together to form limbs (which wrists go with which elbows)
    paf_upsamp = cv2.resize(pafs, (img_orig.shape[1], img_orig.shape[0]), interpolation=cv2.INTER_CUBIC)
    connected_limbs = find_connected_joints(param, paf_upsamp, joint_list_per_joint_type)

    # Step 3: associate limbs that belong to the same person
    person_to_joint_assoc = group_limbs_of_same_person(connected_limbs, joint_list)

    # (Step 4): plot results
    to_plot, canvas = plot_pose(img_orig, joint_list, person_to_joint_assoc)

    return to_plot, canvas, joint_list, person_to_joint_assoc 

def get_img(self):
        while True:
            img_name = self.image_files[self.index]
            label_name = img_name.replace('.jpg', '.png')
            img = cv2.imread(img_name)
            if img is None:
                print("load img failed:", img_name)
                self.next_img()
            else:
                break
        if self.birdeye == True:
            warped_img = cv2.warpPerspective(img, self.M, (4000, 4000),flags=cv2.INTER_CUBIC)
            img   = cv2.resize(warped_img, (self.cols, self.rows), interpolation=cv2.INTER_CUBIC)
        else:
            img   = cv2.resize(img, (self.cols, self.rows), interpolation=cv2.INTER_CUBIC)
        img   = img.transpose((2,0,1))
        return img, label_name 

def _resize_cv2(img, size, interpolation):
    img = img.transpose((1, 2, 0))
    if interpolation == PIL.Image.NEAREST:
        cv_interpolation = cv2.INTER_NEAREST
    elif interpolation == PIL.Image.BILINEAR:
        cv_interpolation = cv2.INTER_LINEAR
    elif interpolation == PIL.Image.BICUBIC:
        cv_interpolation = cv2.INTER_CUBIC
    elif interpolation == PIL.Image.LANCZOS:
        cv_interpolation = cv2.INTER_LANCZOS4
    H, W = size
    img = cv2.resize(img, dsize=(W, H), interpolation=cv_interpolation)

    # If input is a grayscale image, cv2 returns a two-dimentional array.
    if len(img.shape) == 2:
        img = img[:, :, np.newaxis]
    return img.transpose((2, 0, 1)) 

def transform_image(image,ang_range,shear_range,trans_range):

    # Rotation

    ang_rot = np.random.uniform(ang_range)-ang_range/2
    rows,cols,ch = image.shape    
    Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1)

    # Translation
    tr_x = trans_range*np.random.uniform()-trans_range/2
    tr_y = trans_range*np.random.uniform()-trans_range/2
    Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]])

    # Shear
    pts1 = np.float32([[5,5],[20,5],[5,20]])

    pt1 = 5+shear_range*np.random.uniform()-shear_range/2
    pt2 = 20+shear_range*np.random.uniform()-shear_range/2

    pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]])

    shear_M = cv2.getAffineTransform(pts1,pts2)

    image = cv2.warpAffine(image,Rot_M,(cols,rows))
    image = cv2.warpAffine(image,Trans_M,(cols,rows))
    image = cv2.warpAffine(image,shear_M,(cols,rows))

    image = pre_process_image(image.astype(np.uint8))

    #image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)
    #image = image[:,:,0]
    #image = cv2.resize(image, (img_resize,img_resize),interpolation = cv2.INTER_CUBIC)

    return image 

def step(self, amt=1):
        ret, frame = self._vid.read()

        image = cv2.cvtColor(frame, cv2.COLOR_RGB2BGRA)

        if self.crop:
            image = image[self._cropY + self.yoff:self._ih - self._cropY +
                          self.yoff, self._cropX + self.xoff:self._iw - self._cropX + self.xoff]
        else:
            t, b, l, r = self._pad
            image = cv2.copyMakeBorder(
                image, t, b, l, r, cv2.BORDER_CONSTANT, value=[0, 0, 0])

        resized = cv2.resize(image, (self.width, self.height),
                             interpolation=cv2.INTER_CUBIC)
        if self.mirror:
            resized = cv2.flip(resized, 1)

        for y in range(self.height):
            for x in range(self.width):
                self.layout.set(x, y, tuple(resized[y, x][0:3]))

        if not isinstance(self.videoSource, int):
            self._frameCount += 1
            if self._frameCount >= self._frameTotal:
                self._vid.set(1, 0)  # CV_CAP_PROP_POS_FRAMES
                self._frameCount = 0
                self.animComplete = True 

def fixed_crop(src, x0, y0, w, h, size=None, interpolation=cv2.INTER_CUBIC):
    """Crop src at fixed location, and (optionally) resize it to size"""
    out = mx.nd.crop(src, begin=(y0, x0, 0), end=(y0+h, x0+w, int(src.shape[2])))
    if size is not None and (w, h) != size:
        out = resize(out, size, interpolation=interpolation)
    return out 

def one_pic_to_video(image_path, output_video_path, fps, time):
    """
    一张图片合成视频
    one_pic_to_video('./../source/1.jpeg', './../source/output.mp4', 25, 10)
    :param path: 图片文件路径
    :param output_video_path:合成视频的路径
    :param fps:帧率
    :param time:时长
    :return:
    """

    image_clip = ImageClip(image_path)
    img_width, img_height = image_clip.w, image_clip.h

    # 总共的帧数
    frame_num = (int)(fps * time)

    img_size = (int(img_width), int(img_height))

    fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')

    video = cv2.VideoWriter(output_video_path, fourcc, fps, img_size)

    for index in range(frame_num):
        frame = cv2.imread(image_path)
        # 直接缩放到指定大小
        frame_suitable = cv2.resize(frame, (img_size[0], img_size[1]), interpolation=cv2.INTER_CUBIC)

        # 把图片写进视频
        # 重复写入多少次
        video.write(frame_suitable)

    # 释放资源
    video.release()

    return VideoFileClip(output_video_path) 

def preImgOps(self, imgName):
        """
        图像的预处理操作
        :param imgName: 图像的而明朝
        :return: 灰度化和resize之后的图片对象
        """
        strPath = self.strDir + imgName

        img = cv2.imread(strPath)  # 读取图片
        cv2.moveWindow("", 1000, 100)
        # cv2.imshow("原始图", img)
        # 预处理操作
        reImg = cv2.resize(img, (800, 900), interpolation=cv2.INTER_CUBIC)  #
        img2gray = cv2.cvtColor(reImg, cv2.COLOR_BGR2GRAY)  # 将图片压缩为单通道的灰度图
        return img2gray, reImg 

def _resize_subtract_mean(image, insize, rgb_mean):
    interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
    interp_method = interp_methods[random.randrange(5)]
    image = cv2.resize(image, (insize, insize), interpolation=interp_method)
    image = image.astype(np.float32)
    image -= rgb_mean
    return image.transpose(2, 0, 1) 

def undistort(self, img, crop=True):
            undistorted = cv2.resize(cv2.remap(img, self.mapu, self.mapv,
                                               cv2.INTER_LINEAR),
                                     (self.mask.shape[1], self.mask.shape[0]),
                                     interpolation=cv2.INTER_CUBIC)
            if crop:
                undistorted = undistorted[self.y_lim[0]:self.y_lim[1],
                                          self.x_lim[0]:self.x_lim[1]]
            return undistorted 

def load_image(addr):
    # read an image and resize to (224, 224)
    # cv2 load images as BGR, convert it to RGB
    img = cv2.imread(addr)
    if img is None:
        return None
    img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img 

def resizeAndPad(img, size, padColor=0):

    h, w = img.shape[:2]
    sh, sw = size

    # interpolation method
    if h > sh or w > sw: # shrinking image
        interp = cv2.INTER_AREA
    else: # stretching image
        interp = cv2.INTER_CUBIC

    # aspect ratio of image
    aspect = w/h  # if on Python 2, you might need to cast as a float: float(w)/h

    # compute scaling and pad sizing
    if aspect > 1: # horizontal image
        new_w = sw
        new_h = np.round(new_w/aspect).astype(int)
        pad_vert = (sh-new_h)/2
        pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)
        pad_left, pad_right = 0, 0
    elif aspect < 1: # vertical image
        new_h = sh
        new_w = np.round(new_h*aspect).astype(int)
        pad_horz = (sw-new_w)/2
        pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int)
        pad_top, pad_bot = 0, 0
    else: # square image
        new_h, new_w = sh, sw
        pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0

    # set pad color
    if len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided
        padColor = [padColor]*3

    # scale and pad
    scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)
    scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=padColor)

    return scaled_img 

def resizeAndPad(img, size, padColor=0):

    h, w = img.shape[:2]
    sh, sw = size

    # interpolation method
    if h > sh or w > sw: # shrinking image
        interp = cv2.INTER_AREA
    else: # stretching image
        interp = cv2.INTER_CUBIC

    # aspect ratio of image
    aspect = w/h  # if on Python 2, you might need to cast as a float: float(w)/h

    # compute scaling and pad sizing
    if aspect > 1: # horizontal image
        new_w = sw
        new_h = np.round(new_w/aspect).astype(int)
        pad_vert = (sh-new_h)/2
        pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)
        pad_left, pad_right = 0, 0
    elif aspect < 1: # vertical image
        new_h = sh
        new_w = np.round(new_h*aspect).astype(int)
        pad_horz = (sw-new_w)/2
        pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int)
        pad_top, pad_bot = 0, 0
    else: # square image
        new_h, new_w = sh, sw
        pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0

    # set pad color
    if len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided
        padColor = [padColor]*3

    # scale and pad
    scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)
    scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=padColor)

    return scaled_img 

def SplitSingle(self, name, rate, extent):
        img = cv2.imread(os.path.join(self.srcpath, name + extent))
        assert np.shape(img) != ()

        if (rate != 1):
            resizeimg = cv2.resize(img, None, fx=rate, fy=rate, interpolation=cv2.INTER_CUBIC)
        else:
            resizeimg = img
        outbasename = name + '__' + str(rate) + '__'

        weight = np.shape(resizeimg)[1]
        height = np.shape(resizeimg)[0]

        # if (max(weight, height) < self.subsize/2):
        #     return

        left, up = 0, 0
        while (left < weight):
            if (left + self.subsize >= weight):
                left = max(weight - self.subsize, 0)
            up = 0
            while (up < height):
                if (up + self.subsize >= height):
                    up = max(height - self.subsize, 0)
                subimgname = outbasename + str(left) + '___' + str(up)
                self.saveimagepatches(resizeimg, subimgname, left, up)
                if (up + self.subsize >= height):
                    break
                else:
                    up = up + self.slide
            if (left + self.subsize >= weight):
                break
            else:
                left = left + self.slide 

def SplitSingle(self, name, rate, extent):
        img = cv2.imread(os.path.join(self.srcpath, name + extent), cv2.IMREAD_UNCHANGED)
        assert np.shape(img) != ()

        if (rate != 1):
            resizeimg = cv2.resize(img, None, fx=rate, fy=rate, interpolation = cv2.INTER_CUBIC)
        else:
            resizeimg = img
        outbasename = name + '__' + str(rate) + '__'

        weight = np.shape(resizeimg)[1]
        height = np.shape(resizeimg)[0]
        
        left, up = 0, 0
        while (left < weight):
            if (left + self.subsize >= weight):
                left = max(weight - self.subsize, 0)
            up = 0
            while (up < height):
                if (up + self.subsize >= height):
                    up = max(height - self.subsize, 0)
                subimgname = outbasename + str(left) + '___' + str(up)
                self.saveimagepatches(resizeimg, subimgname, left, up)
                if (up + self.subsize >= height):
                    break
                else:
                    up = up + self.slide
            if (left + self.subsize >= weight):
                break
            else:
                left = left + self.slide 

def resize(img, size, interpolation=cv2.INTER_LINEAR):
    r"""Resize the input numpy ndarray to the given size.
    Args:
        img (numpy ndarray): Image to be resized.
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), the output size will be matched to this. If size is an int,
            the smaller edge of the image will be matched to this number maintaing
            the aspect ratio. i.e, if height > width, then image will be rescaled to
            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`
        interpolation (int, optional): Desired interpolation. Default is
            ``cv2.INTER_CUBIC``
    Returns:
        PIL Image: Resized image.
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy image. Got {}'.format(type(img)))
    if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
        raise TypeError('Got inappropriate size arg: {}'.format(size))
    w, h, =  size

    if isinstance(size, int):
        if (w <= h and w == size) or (h <= w and h == size):
            return img
        if w < h:
            ow = size
            oh = int(size * h / w)
            output = cv2.resize(img, dsize=(ow, oh), interpolation=interpolation)
        else:
            oh = size
            ow = int(size * w / h)
            output = cv2.resize(img, dsize=(ow, oh), interpolation=interpolation)
    else:
        output = cv2.resize(img, dsize=(size[1], size[0]), interpolation=interpolation)
    if img.shape[2]==1:
        return output[:, :, np.newaxis]
    else:
        return output 

def affine(img, angle, translate, scale, shear, interpolation=cv2.INTER_LINEAR, mode=cv2.BORDER_CONSTANT, fillcolor=0):
    """Apply affine transformation on the image keeping image center invariant
    Args:
        img (numpy ndarray): numpy ndarray to be transformed.
        angle (float or int): rotation angle in degrees between -180 and 180, clockwise direction.
        translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
        scale (float): overall scale
        shear (float): shear angle value in degrees between -180 to 180, clockwise direction.
        interpolation (``cv2.INTER_NEAREST` or ``cv2.INTER_LINEAR`` or ``cv2.INTER_AREA``, ``cv2.INTER_CUBIC``):
            An optional resampling filter.
            See `filters`_ for more information.
            If omitted, it is set to ``cv2.INTER_CUBIC``, for bicubic interpolation.
        mode (``cv2.BORDER_CONSTANT`` or ``cv2.BORDER_REPLICATE`` or ``cv2.BORDER_REFLECT`` or ``cv2.BORDER_REFLECT_101``)
            Method for filling in border regions. 
            Defaults to cv2.BORDER_CONSTANT, meaning areas outside the image are filled with a value (val, default 0)
        val (int): Optional fill color for the area outside the transform in the output image. Default: 0
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy Image. Got {}'.format(type(img)))

    assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
        "Argument translate should be a list or tuple of length 2"

    assert scale > 0.0, "Argument scale should be positive"

    output_size = img.shape[0:2]
    center = (img.shape[1] * 0.5 + 0.5, img.shape[0] * 0.5 + 0.5)
    matrix = _get_affine_matrix(center, angle, translate, scale, shear)
    
    if img.shape[2]==1:
        return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor)[:,:,np.newaxis]
    else:
        return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor) 

def fbresnet_augmentor(isTrain):
    """
    Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
    """
    if isTrain:
        augmentors = [
            GoogleNetResize(),
            # It's OK to remove the following augs if your CPU is not fast enough.
            # Removing brightness/contrast/saturation does not have a significant effect on accuracy.
            # Removing lighting leads to a tiny drop in accuracy.
            imgaug.RandomOrderAug(
                [imgaug.BrightnessScale((0.6, 1.4), clip=False),
                 imgaug.Contrast((0.6, 1.4), clip=False),
                 imgaug.Saturation(0.4, rgb=False),
                 # rgb-bgr conversion for the constants copied from fb.resnet.torch
                 imgaug.Lighting(0.1,
                                 eigval=np.asarray(
                                     [0.2175, 0.0188, 0.0045][::-1]) * 255.0,
                                 eigvec=np.array(
                                     [[-0.5675, 0.7192, 0.4009],
                                      [-0.5808, -0.0045, -0.8140],
                                      [-0.5836, -0.6948, 0.4203]],
                                     dtype='float32')[::-1, ::-1]
                                 )]),
            imgaug.Flip(horiz=True),
        ]
    else:
        augmentors = [
            imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
            imgaug.CenterCrop((224, 224)),
        ]
    return augmentors 

def get_img(path, size=(64, 64), interp=cv2.INTER_CUBIC):
        img = cv2.imread(path, cv2.IMREAD_COLOR)[..., ::-1]  # BGR to RGB
        if img.shape[:1] == size:
            return img
        else:
            return cv2.resize(img, size, interp)