# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for OWLv2.""" import warnings from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_to_corners_format, pad, to_channel_dimension_format, ) from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import ( TensorType, filter_out_non_signature_kwargs, is_scipy_available, is_torch_available, is_vision_available, logging, requires_backends, ) if is_torch_available(): import torch if is_vision_available(): import PIL if is_scipy_available(): from scipy import ndimage as ndi if TYPE_CHECKING: from .modeling_owlv2 import Owlv2ObjectDetectionOutput logger = logging.get_logger(__name__) def _scale_boxes(boxes, target_sizes): """ Scale batch of bounding boxes to the target sizes. Args: boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. target_sizes (`List[Tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. Returns: `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. """ if isinstance(target_sizes, (list, tuple)): image_height = torch.tensor([i[0] for i in target_sizes]) image_width = torch.tensor([i[1] for i in target_sizes]) elif isinstance(target_sizes, torch.Tensor): image_height, image_width = target_sizes.unbind(1) else: raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor") # for owlv2 image is padded to max size unlike owlvit, thats why we have to scale boxes to max size max_size = torch.max(image_height, image_width) scale_factor = torch.stack([max_size, max_size, max_size, max_size], dim=1) scale_factor = scale_factor.unsqueeze(1).to(boxes.device) boxes = boxes * scale_factor return boxes # Copied from transformers.models.owlvit.image_processing_owlvit._upcast def _upcast(t): # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type if t.is_floating_point(): return t if t.dtype in (torch.float32, torch.float64) else t.float() else: return t if t.dtype in (torch.int32, torch.int64) else t.int() # Copied from transformers.models.owlvit.image_processing_owlvit.box_area def box_area(boxes): """ Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. Args: boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`. Returns: `torch.FloatTensor`: a tensor containing the area for each box. """ boxes = _upcast(boxes) return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # Copied from transformers.models.owlvit.image_processing_owlvit.box_iou def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union def _preprocess_resize_output_shape(image, output_shape): """Validate resize output shape according to input image. Args: image (`np.ndarray`): Image to be resized. output_shape (`iterable`): Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of channels is preserved. Returns image (`np.ndarray`): The input image, but with additional singleton dimensions appended in the case where `len(output_shape) > input.ndim`. output_shape (`Tuple`): The output shape converted to tuple. Raises ------ ValueError: If output_shape length is smaller than the image number of dimensions. Notes ----- The input image is reshaped if its number of dimensions is not equal to output_shape_length. """ output_shape = tuple(output_shape) output_ndim = len(output_shape) input_shape = image.shape if output_ndim > image.ndim: # append dimensions to input_shape input_shape += (1,) * (output_ndim - image.ndim) image = np.reshape(image, input_shape) elif output_ndim == image.ndim - 1: # multichannel case: append shape of last axis output_shape = output_shape + (image.shape[-1],) elif output_ndim < image.ndim: raise ValueError("output_shape length cannot be smaller than the image number of dimensions") return image, output_shape def _clip_warp_output(input_image, output_image): """Clip output image to range of values of input image. Note that this function modifies the values of *output_image* in-place. Taken from: https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640. Args: input_image : ndarray Input image. output_image : ndarray Output image, which is modified in-place. """ min_val = np.min(input_image) if np.isnan(min_val): # NaNs detected, use NaN-safe min/max min_func = np.nanmin max_func = np.nanmax min_val = min_func(input_image) else: min_func = np.min max_func = np.max max_val = max_func(input_image) output_image = np.clip(output_image, min_val, max_val) return output_image class Owlv2ImageProcessor(BaseImageProcessor): r""" Constructs an OWLv2 image processor. Args: do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess` method. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to a square with gray pixels on the bottom and the right. Can be overriden by `do_pad` in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 960, "width": 960}`): Size to resize the image to. Can be overriden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling method to use if resizing the image. Can be overriden by `resample` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_pad: bool = True, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad self.do_resize = do_resize self.size = size if size is not None else {"height": 960, "width": 960} self.resample = resample self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD def pad( self, image: np.array, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """ Pad an image to a square with gray pixels on the bottom and the right, as per the original OWLv2 implementation. Args: image (`np.ndarray`): Image to pad. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. """ height, width = get_image_size(image) size = max(height, width) image = pad( image=image, padding=((0, size - height), (0, size - width)), constant_values=0.5, data_format=data_format, input_data_format=input_data_format, ) return image def resize( self, image: np.ndarray, size: Dict[str, int], anti_aliasing: bool = True, anti_aliasing_sigma=None, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image as per the original implementation. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary containing the height and width to resize the image to. anti_aliasing (`bool`, *optional*, defaults to `True`): Whether to apply anti-aliasing when downsampling the image. anti_aliasing_sigma (`float`, *optional*, defaults to `None`): Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated automatically. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. """ requires_backends(self, "scipy") output_shape = (size["height"], size["width"]) image = to_channel_dimension_format(image, ChannelDimension.LAST) image, output_shape = _preprocess_resize_output_shape(image, output_shape) input_shape = image.shape factors = np.divide(input_shape, output_shape) # Translate modes used by np.pad to those used by scipy.ndimage ndi_mode = "mirror" cval = 0 order = 1 if anti_aliasing: if anti_aliasing_sigma is None: anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2) else: anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(factors) if np.any(anti_aliasing_sigma < 0): raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero") elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)): warnings.warn( "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes" ) filtered = ndi.gaussian_filter(image, anti_aliasing_sigma, cval=cval, mode=ndi_mode) else: filtered = image zoom_factors = [1 / f for f in factors] out = ndi.zoom(filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True) image = _clip_warp_output(image, out) image = to_channel_dimension_format(image, input_data_format, ChannelDimension.LAST) image = ( to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image ) return image @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, do_pad: Optional[bool] = None, do_resize: Optional[bool] = None, size: Dict[str, int] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image to a square with gray pixels on the bottom and the right. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size to resize the image to. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_pad = do_pad if do_pad is not None else self.do_pad do_resize = do_resize if do_resize is not None else self.do_resize do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = size if size is not None else self.size size = get_size_dict(size) # for BC images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) # Here, pad and resize methods are different from the rest of image processors # as they don't have any resampling in resize() # or pad size in pad() (the maximum of (height, width) is taken instead). # hence, these arguments don't need to be passed in validate_preprocess_arguments. validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, size=size, ) # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_rescale and is_scaled_image(images[0]): logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_rescale: images = [ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images ] if do_pad: images = [self.pad(image=image, input_data_format=input_data_format) for image in images] if do_resize: images = [ self.resize( image=image, size=size, input_data_format=input_data_format, ) for image in images ] if do_normalize: images = [ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) for image in images ] images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors) # Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_object_detection with OwlViT->Owlv2 def post_process_object_detection( self, outputs: "Owlv2ObjectDetectionOutput", threshold: float = 0.1, target_sizes: Optional[Union[TensorType, List[Tuple]]] = None, ): """ Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Args: outputs ([`Owlv2ObjectDetectionOutput`]): Raw outputs of the model. threshold (`float`, *optional*, defaults to 0.1): Score threshold to keep object detection predictions. target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. If unset, predictions will not be resized. Returns: `List[Dict]`: A list of dictionaries, each dictionary containing the following keys: - "scores": The confidence scores for each predicted box on the image. - "labels": Indexes of the classes predicted by the model on the image. - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. """ batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes batch_size = len(batch_logits) if target_sizes is not None and len(target_sizes) != batch_size: raise ValueError("Make sure that you pass in as many target sizes as images") # batch_logits of shape (batch_size, num_queries, num_classes) batch_class_logits = torch.max(batch_logits, dim=-1) batch_scores = torch.sigmoid(batch_class_logits.values) batch_labels = batch_class_logits.indices # Convert to [x0, y0, x1, y1] format batch_boxes = center_to_corners_format(batch_boxes) # Convert from relative [0, 1] to absolute [0, height] coordinates if target_sizes is not None: batch_boxes = _scale_boxes(batch_boxes, target_sizes) results = [] for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes): keep = scores > threshold scores = scores[keep] labels = labels[keep] boxes = boxes[keep] results.append({"scores": scores, "labels": labels, "boxes": boxes}) return results # Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_image_guided_detection def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None): """ Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO api. Args: outputs ([`OwlViTImageGuidedObjectDetectionOutput`]): Raw outputs of the model. threshold (`float`, *optional*, defaults to 0.0): Minimum confidence threshold to use to filter out predicted boxes. nms_threshold (`float`, *optional*, defaults to 0.3): IoU threshold for non-maximum suppression of overlapping boxes. target_sizes (`torch.Tensor`, *optional*): Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to None, predictions will not be unnormalized. Returns: `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image in the batch as predicted by the model. All labels are set to None as `OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection. """ logits, target_boxes = outputs.logits, outputs.target_pred_boxes if target_sizes is not None and len(logits) != len(target_sizes): raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") if target_sizes is not None and target_sizes.shape[1] != 2: raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") probs = torch.max(logits, dim=-1) scores = torch.sigmoid(probs.values) # Convert to [x0, y0, x1, y1] format target_boxes = center_to_corners_format(target_boxes) # Apply non-maximum suppression (NMS) if nms_threshold < 1.0: for idx in range(target_boxes.shape[0]): for i in torch.argsort(-scores[idx]): if not scores[idx][i]: continue ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0] ious[i] = -1.0 # Mask self-IoU. scores[idx][ious > nms_threshold] = 0.0 # Convert from relative [0, 1] to absolute [0, height] coordinates if target_sizes is not None: target_boxes = _scale_boxes(target_boxes, target_sizes) # Compute box display alphas based on prediction scores results = [] alphas = torch.zeros_like(scores) for idx in range(target_boxes.shape[0]): # Select scores for boxes matching the current query: query_scores = scores[idx] if not query_scores.nonzero().numel(): continue # Apply threshold on scores before scaling query_scores[query_scores < threshold] = 0.0 # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1. # All other boxes will either belong to a different query, or will not be shown. max_score = torch.max(query_scores) + 1e-6 query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9) query_alphas = torch.clip(query_alphas, 0.0, 1.0) alphas[idx] = query_alphas mask = alphas[idx] > 0 box_scores = alphas[idx][mask] boxes = target_boxes[idx][mask] results.append({"scores": box_scores, "labels": None, "boxes": boxes}) return results __all__ = ["Owlv2ImageProcessor"]