Python cv2.INTER_CUBIC属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import INTER_CUBIC [as 别名]
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