U hB@sddlmZddlZddlZddlmZmZmZddlZddl Z ddl m Z ddl mZddlmZddlmZddlmZmZmZdd lmZmZdd lmZmZmZd dlmZ d d dgZ!dZ"Gdd d eZ#GdddeZ$Gdd d eZ%GdddeZ&dS)) annotationsN)AnyTuplecast)ProjectiveTransform)Literal) functional)Affine)BorderModeTypeInterpolationTypeSymmetricRangeType)BaseTransformInitSchema DualTransform) ColorTypeScaleFloatTypeTargetsRotateRandomRotate90 SafeRotateg|=c@s|eZdZdZejejejejfZ dddddddZ dd d d Z ddddd d dZ dddddddZ dd ddZdS)rzRandomly rotate the input by 90 degrees zero or more times. Args: p: probability of applying the transform. Default: 0.5. Targets: image, mask, bboxes, keypoints Image types: uint8, float32 np.ndarrayintr)imgfactorparamsreturncKs t||SN) fgeometricZrot90)selfrrrrQ/tmp/pip-unpacked-wheel-e8onvpoz/albumentations/augmentations/geometric/rotate.pyapply-szRandomRotate90.applydict[str, int]rcCsdtddiS)Nrr)randomrandintrrrr get_params0szRandomRotate90.get_params)bboxesrrrcKs t||Sr)rZ bboxes_rot90)rr)rrrrr apply_to_bboxes4szRandomRotate90.apply_to_bboxes) keypointsrrrcKst|||dS)Nshape)rZkeypoints_rot90)rr+rrrrr apply_to_keypoints7sz!RandomRotate90.apply_to_keypointsz tuple[()]cCsdS)Nrrr'rrr get_transform_init_args_names:sz,RandomRotate90.get_transform_init_args_namesN)__name__ __module__ __qualname____doc__rIMAGEMASKBBOXES KEYPOINTS_targetsr!r(r*r-r.rrrr rs c@s6eZdZUded<ded<ded<ded<ded <d S) RotateInitSchemar limitr interpolationr border_modeColorType | Nonevalue mask_valueNr/r0r1__annotations__rrrr r8>s r8c seZdZdZejejejejfZ Gddde Z de j e jddddddf d d d d d d d ddd fdd Zddd d d d dddddZddd d d d dddddZddd d d d dddddZddd d d d ddddd Zed d dd!d"d#d$Zd%d%d%d&d'd(Zd)d*d+d,ZZS)-ru Rotate the input by an angle selected randomly from the uniform distribution. Args: limit (float | tuple[float, float]): Range from which a random angle is picked. If limit is a single float, an angle is picked from (-limit, limit). Default: (-90, 90) interpolation (OpenCV flag): Flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. border_mode (OpenCV flag): Flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 value (int, float, list of ints, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT. mask_value (int, float, list of ints, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. rotate_method (str): Method to rotate bounding boxes. Should be 'largest_box' or 'ellipse'. Default: 'largest_box' crop_border (bool): Whether to crop border after rotation. If True, the output image size might differ from the input. Default: False p (float): Probability of applying the transform. Default: 0.5. Targets: image, mask, bboxes, keypoints Image types: uint8, float32 Note: - The rotation angle is randomly selected for each execution within the range specified by 'limit'. - When 'crop_border' is False, the output image will have the same size as the input, potentially introducing black triangles in the corners. - When 'crop_border' is True, the output image is cropped to remove black triangles, which may result in a smaller image. - Bounding boxes are rotated and may change size or shape. - Keypoints are rotated around the center of the image. Mathematical Details: 1. An angle θ is randomly sampled from the range specified by 'limit'. 2. The image is rotated around its center by θ degrees. 3. The rotation matrix R is: R = [cos(θ) -sin(θ)] [sin(θ) cos(θ)] 4. Each point (x, y) in the image is transformed to (x', y') by: [x'] [cos(θ) -sin(θ)] [x - cx] [cx] [y'] = [sin(θ) cos(θ)] [y - cy] + [cy] where (cx, cy) is the center of the image. 5. If 'crop_border' is True, the image is cropped to the largest rectangle that fits inside the rotated image. Example: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> transform = A.Rotate(limit=45, p=1.0) >>> result = transform(image=image) >>> rotated_image = result['image'] # rotated_image will be the input image rotated by a random angle between -45 and 45 degrees c@seZdZUded<ded<dS)zRotate.InitSchema#Literal[('largest_box', 'ellipse')] rotate_methodbool crop_borderNr?rrrr InitSchemas rEiZN largest_boxF?rrr<rArC bool | Nonefloat) r9r:r;r=r>rBrD always_applypc sLtj| |dttttf||_||_||_||_||_ ||_ ||_ dS)N)rMrL) super__init__rrrKr9r:r;r=r>rBrD) rr9r:r;r=r>rBrDrLrM __class__rr rOs zRotate.__init__rr)ranglex_minx_maxy_miny_maxrrc Ks4t|||j|j|j}|jr0t|||||S|Sr)rrotater:r;r=rDfcropscrop) rrrRrSrTrUrVrimg_outrrr r!s z Rotate.apply)maskrRrSrTrUrVrrc Ks4t||tj|j|j}|jr0t|||||S|Sr) rrWcv2Z INTER_NEARESTr;r>rDrXrY) rr[rRrSrTrUrVrrZrrr apply_to_masks zRotate.apply_to_mask)r)rRrSrTrUrVrrc KsB|ddd}t|||j|} |jr>t| ||||f|S| SNr,)rZ bboxes_rotaterBrDrXZcrop_bboxes_by_coords) rr)rRrSrTrUrVr image_shapeZ bboxes_outrrr r*s zRotate.apply_to_bboxes)r+rRrSrTrUrVrrc Ks8t|||ddd}|jr4t|||||fS|Sr^)rZkeypoints_rotaterDrXZcrop_keypoints_by_coords) rr+rRrSrTrUrVrZ keypoints_outrrr r-s zRotate.apply_to_keypointsr")heightwidthrRrc Cs>t|}||k}|r||fn||f\}}tt|tt|}}|d|||kslt||tkrd|}|r||||fn||||f\} } n:||||} ||||| ||||| } } tdt|d| dt|t|d| dtdt|d| dt|t|d| ddS)aTGiven a rectangle of size wxh that has been rotated by 'angle' (in degrees), computes the width and height of the largest possible axis-aligned rectangle (maximal area) within the rotated rectangle. Reference: https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders g@rIrr_rSrTrUrV) mathradiansabssincos SMALL_NUMBERmaxrmin) rarbrRZwidth_is_longerZ side_longZ side_shortZsin_aZcos_axwrhrZcos_2arrr _rotated_rect_with_max_areas $**z"Rotate._rotated_rect_with_max_areadict[str, Any]rdatarcCs\dtj|ji}|jrD|ddd\}}|||||dn|ddddd|S)NrRr,r_rc)r%uniformr9rDupdatero)rrrrZ out_paramsrarbrrr get_params_dependent_on_datas z#Rotate.get_params_dependent_on_datatuple[str, ...]r#cCsdS)N)r9r:r;r=r>rBrDrr'rrr r.sz$Rotate.get_transform_init_args_names)r/r0r1r2rr3r4r5r6r7r8rEr\ INTER_LINEARBORDER_REFLECT_101rOr!r]r*r- staticmethodrorvr. __classcell__rrrPr rHs*9$  c seZdZdZejejejejfZ Gddde Z de j e jdddddfdd d d d d d d dfdd ZddddZd ddddddZddddddZZS)rut Rotate the input inside the input's frame by an angle selected randomly from the uniform distribution. This transformation ensures that the entire rotated image fits within the original frame by scaling it down if necessary. The resulting image maintains its original dimensions but may contain artifacts due to the rotation and scaling process. Args: limit (float | tuple[float, float]): Range from which a random angle is picked. If limit is a single float, an angle is picked from (-limit, limit). Default: (-90, 90) interpolation (OpenCV flag): Flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. border_mode (OpenCV flag): Flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 value (int, float, list of int, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT. mask_value (int, float, list of int, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. rotate_method (str): Method to rotate bounding boxes. Should be 'largest_box' or 'ellipse'. Default: 'largest_box' p (float): Probability of applying the transform. Default: 0.5. Targets: image, mask, bboxes, keypoints Image types: uint8, float32 Note: - The rotation is performed around the center of the image. - After rotation, the image is scaled to fit within the original frame, which may cause some distortion. - The output image will always have the same dimensions as the input image. - Bounding boxes and keypoints are transformed along with the image. Mathematical Details: 1. An angle θ is randomly sampled from the range specified by 'limit'. 2. The image is rotated around its center by θ degrees. 3. The rotation matrix R is: R = [cos(θ) -sin(θ)] [sin(θ) cos(θ)] 4. The scaling factor s is calculated to ensure the rotated image fits within the original frame: s = min(width / (width * |cos(θ)| + height * |sin(θ)|), height / (width * |sin(θ)| + height * |cos(θ)|)) 5. The combined transformation matrix T is: T = [s*cos(θ) -s*sin(θ) tx] [s*sin(θ) s*cos(θ) ty] where tx and ty are translation factors to keep the image centered. 6. Each point (x, y) in the image is transformed to (x', y') by: [x'] [s*cos(θ) -s*sin(θ)] [x - cx] [cx] [y'] = [s*sin(θ) s*cos(θ)] [y - cy] + [cy] where (cx, cy) is the center of the image. Example: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> transform = A.SafeRotate(limit=45, p=1.0) >>> result = transform(image=image) >>> rotated_image = result['image'] # rotated_image will be the input image rotated by a random angle between -45 and 45 degrees, # scaled to fit within the original 100x100 frame c@seZdZUded<dS)zSafeRotate.InitSchemarArBNr?rrrr rEKs rErFNrHrIrrr<rArJrK)r9r:r;r=r>rBrLrMc st|dkr dn|}|dkrdn|}tj||||||d||d ttttf||_||_||_||_||_ ||_ dS)NrT) rWr:modecvalZ cval_maskrBZ fit_outputrMrL) rNrOrrrKr9r:r;r=r>rB) rr9r:r;r=r>rBrLrMrPrr rONs&  zSafeRotate.__init__rwr#cCsdS)N)r9r:r;r=r>rBrr'rrr r.msz(SafeRotate.get_transform_init_args_namesztuple[float, float]ztuple[int, int]z,tuple[ProjectiveTransform, dict[str, float]])rRcenterr`rcCs|dd\}}t||d}t|d}t|d}t||||} t||||} |d| d|d7<|d| d|d7<|| } || } t| ddgd| dgdddgg} | t|dddgg}t|d | | d fS) Nr_g?)rr)rr)rr_r)rr_r)matrix)rly)r\ZgetRotationMatrix2DrfrnparrayZvstackr)rrRr~r`rarbZ rotation_matZabs_cosZabs_sinZnew_wZnew_hZscale_xZscale_yZ scale_matrrrr _create_safe_rotate_matrixps  "z%SafeRotate._create_safe_rotate_matrixrprqc Csp|ddd}t|jd|jd}t|}t|}||||\}}||||\} } |||| |dS)Nr,r_rr)rWscaler bbox_matrixZ output_shape)r%rtr9rr~Z center_bboxr) rrrrr`rRZ image_centerZ bbox_centerrrr_rrr rvs  z'SafeRotate.get_params_dependent_on_data)r/r0r1r2rr3r4r5r6r7r8rEr\rxryrOr.rrvr{rrrPr r s?")' __future__rrdr%typingrrrr\ZnumpyrZskimage.transformrZtyping_extensionsrZ"albumentations.augmentations.cropsrrXZ1albumentations.augmentations.geometric.transformsr Zalbumentations.core.pydanticr r r Z(albumentations.core.transforms_interfacer rZalbumentations.core.typesrrrr__all__rirr8rrrrrr s(       ! 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