processing_viscpmchatbee.py

import cv2
import numpy as np
import torch
from timm.data.constants import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.data.transforms import RandomResizedCropAndInterpolation
from torchvision import transforms
import urllib
from tqdm import tqdm
from cpm_live.tokenizers import CPMBeeTokenizer
from torch.utils.data import default_collate
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from typing_extensions import TypedDict
from numpy.typing import NDArray
import importlib.machinery
import importlib.util
import types
import random
from transformers.image_utils import make_list_of_images
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from transformers import TensorType
import json


# aug functions
def identity_func(img):
    return img


def autocontrast_func(img, cutoff=0):
    '''
        same output as PIL.ImageOps.autocontrast
    '''
    n_bins = 256

    def tune_channel(ch):
        n = ch.size
        cut = cutoff * n // 100
        if cut == 0:
            high, low = ch.max(), ch.min()
        else:
            hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
            low = np.argwhere(np.cumsum(hist) > cut)
            low = 0 if low.shape[0] == 0 else low[0]
            high = np.argwhere(np.cumsum(hist[::-1]) > cut)
            high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
        if high <= low:
            table = np.arange(n_bins)
        else:
            scale = (n_bins - 1) / (high - low)
            table = np.arange(n_bins) * scale - low * scale
            table[table < 0] = 0
            table[table > n_bins - 1] = n_bins - 1
        table = table.clip(0, 255).astype(np.uint8)
        return table[ch]

    channels = [tune_channel(ch) for ch in cv2.split(img)]
    out = cv2.merge(channels)
    return out


def equalize_func(img):
    '''
        same output as PIL.ImageOps.equalize
        PIL's implementation is different from cv2.equalize
    '''
    n_bins = 256

    def tune_channel(ch):
        hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
        non_zero_hist = hist[hist != 0].reshape(-1)
        step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
        if step == 0:
            return ch
        n = np.empty_like(hist)
        n[0] = step // 2
        n[1:] = hist[:-1]
        table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
        return table[ch]

    channels = [tune_channel(ch) for ch in cv2.split(img)]
    out = cv2.merge(channels)
    return out


def rotate_func(img, degree, fill=(0, 0, 0)):
    '''
    like PIL, rotate by degree, not radians
    '''
    H, W = img.shape[0], img.shape[1]
    center = W / 2, H / 2
    M = cv2.getRotationMatrix2D(center, degree, 1)
    out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
    return out


def solarize_func(img, thresh=128):
    '''
        same output as PIL.ImageOps.posterize
    '''
    table = np.array([el if el < thresh else 255 - el for el in range(256)])
    table = table.clip(0, 255).astype(np.uint8)
    out = table[img]
    return out


def color_func(img, factor):
    '''
        same output as PIL.ImageEnhance.Color
    '''
    # implementation according to PIL definition, quite slow
    #  degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
    #  out = blend(degenerate, img, factor)
    #  M = (
    #      np.eye(3) * factor
    #      + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
    #  )[np.newaxis, np.newaxis, :]
    M = (
        np.float32([
            [0.886, -0.114, -0.114],
            [-0.587, 0.413, -0.587],
            [-0.299, -0.299, 0.701]]) * factor
        + np.float32([[0.114], [0.587], [0.299]])
    )
    out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
    return out


def contrast_func(img, factor):
    """
        same output as PIL.ImageEnhance.Contrast
    """
    mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
    table = np.array([(
        el - mean) * factor + mean
        for el in range(256)
    ]).clip(0, 255).astype(np.uint8)
    out = table[img]
    return out


def brightness_func(img, factor):
    '''
        same output as PIL.ImageEnhance.Contrast
    '''
    table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
    out = table[img]
    return out


def sharpness_func(img, factor):
    '''
    The differences the this result and PIL are all on the 4 boundaries, the center
    areas are same
    '''
    kernel = np.ones((3, 3), dtype=np.float32)
    kernel[1][1] = 5
    kernel /= 13
    degenerate = cv2.filter2D(img, -1, kernel)
    if factor == 0.0:
        out = degenerate
    elif factor == 1.0:
        out = img
    else:
        out = img.astype(np.float32)
        degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
        out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
        out = out.astype(np.uint8)
    return out


def shear_x_func(img, factor, fill=(0, 0, 0)):
    H, W = img.shape[0], img.shape[1]
    M = np.float32([[1, factor, 0], [0, 1, 0]])
    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
    return out


def translate_x_func(img, offset, fill=(0, 0, 0)):
    '''
        same output as PIL.Image.transform
    '''
    H, W = img.shape[0], img.shape[1]
    M = np.float32([[1, 0, -offset], [0, 1, 0]])
    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
    return out


def translate_y_func(img, offset, fill=(0, 0, 0)):
    '''
        same output as PIL.Image.transform
    '''
    H, W = img.shape[0], img.shape[1]
    M = np.float32([[1, 0, 0], [0, 1, -offset]])
    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
    return out


def posterize_func(img, bits):
    '''
        same output as PIL.ImageOps.posterize
    '''
    out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
    return out


def shear_y_func(img, factor, fill=(0, 0, 0)):
    H, W = img.shape[0], img.shape[1]
    M = np.float32([[1, 0, 0], [factor, 1, 0]])
    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
    return out


def cutout_func(img, pad_size, replace=(0, 0, 0)):
    replace = np.array(replace, dtype=np.uint8)
    H, W = img.shape[0], img.shape[1]
    rh, rw = np.random.random(2)
    pad_size = pad_size // 2
    ch, cw = int(rh * H), int(rw * W)
    x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
    y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
    out = img.copy()
    out[x1:x2, y1:y2, :] = replace
    return out


# level to args
def enhance_level_to_args(MAX_LEVEL):
    def level_to_args(level):
        return ((level / MAX_LEVEL) * 1.8 + 0.1,)
    return level_to_args


def shear_level_to_args(MAX_LEVEL, replace_value):
    def level_to_args(level):
        level = (level / MAX_LEVEL) * 0.3
        if np.random.random() > 0.5:
            level = -level
        return (level, replace_value)

    return level_to_args


def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
    def level_to_args(level):
        level = (level / MAX_LEVEL) * float(translate_const)
        if np.random.random() > 0.5:
            level = -level
        return (level, replace_value)

    return level_to_args


def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
    def level_to_args(level):
        level = int((level / MAX_LEVEL) * cutout_const)
        return (level, replace_value)

    return level_to_args


def solarize_level_to_args(MAX_LEVEL):
    def level_to_args(level):
        level = int((level / MAX_LEVEL) * 256)
        return (level, )
    return level_to_args


def none_level_to_args(level):
    return ()


def posterize_level_to_args(MAX_LEVEL):
    def level_to_args(level):
        level = int((level / MAX_LEVEL) * 4)
        return (level, )
    return level_to_args


def rotate_level_to_args(MAX_LEVEL, replace_value):
    def level_to_args(level):
        level = (level / MAX_LEVEL) * 30
        if np.random.random() < 0.5:
            level = -level
        return (level, replace_value)

    return level_to_args


func_dict = {
    'Identity': identity_func,
    'AutoContrast': autocontrast_func,
    'Equalize': equalize_func,
    'Rotate': rotate_func,
    'Solarize': solarize_func,
    'Color': color_func,
    'Contrast': contrast_func,
    'Brightness': brightness_func,
    'Sharpness': sharpness_func,
    'ShearX': shear_x_func,
    'TranslateX': translate_x_func,
    'TranslateY': translate_y_func,
    'Posterize': posterize_func,
    'ShearY': shear_y_func,
}

translate_const = 10
MAX_LEVEL = 10
replace_value = (128, 128, 128)
arg_dict = {
    'Identity': none_level_to_args,
    'AutoContrast': none_level_to_args,
    'Equalize': none_level_to_args,
    'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value),
    'Solarize': solarize_level_to_args(MAX_LEVEL),
    'Color': enhance_level_to_args(MAX_LEVEL),
    'Contrast': enhance_level_to_args(MAX_LEVEL),
    'Brightness': enhance_level_to_args(MAX_LEVEL),
    'Sharpness': enhance_level_to_args(MAX_LEVEL),
    'ShearX': shear_level_to_args(MAX_LEVEL, replace_value),
    'TranslateX': translate_level_to_args(
        translate_const, MAX_LEVEL, replace_value
    ),
    'TranslateY': translate_level_to_args(
        translate_const, MAX_LEVEL, replace_value
    ),
    'Posterize': posterize_level_to_args(MAX_LEVEL),
    'ShearY': shear_level_to_args(MAX_LEVEL, replace_value),
}


class RandomAugment(object):

    def __init__(self, N=2, M=10, isPIL=False, augs=[]):
        self.N = N
        self.M = M
        self.isPIL = isPIL
        if augs:
            self.augs = augs
        else:
            self.augs = list(arg_dict.keys())

    def get_random_ops(self):
        sampled_ops = np.random.choice(self.augs, self.N)
        return [(op, 0.5, self.M) for op in sampled_ops]

    def __call__(self, img):
        if self.isPIL:
            img = np.array(img)
        ops = self.get_random_ops()
        for name, prob, level in ops:
            if np.random.random() > prob:
                continue
            args = arg_dict[name](level)
            img = func_dict[name](img, *args)
        return img


def build_transform(is_train, randaug=True, input_size=224, interpolation='bicubic'):
    if is_train:
        t = [
            RandomResizedCropAndInterpolation(
                input_size, scale=(0.5, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
            transforms.RandomHorizontalFlip(),
        ]
        if randaug:
            t.append(
                RandomAugment(
                    2, 7, isPIL=True,
                    augs=[
                        'Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness',
                        'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate',
                    ]))
        t += [
            transforms.ToTensor(),
            transforms.Normalize(mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
        ]
        t = transforms.Compose(t)
    else:
        t = transforms.Compose([
            transforms.Resize((input_size, input_size),
                              interpolation=transforms.InterpolationMode.BICUBIC),
            transforms.ToTensor(),
            transforms.Normalize(mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD)
        ])

    return t


class VisCpmChatBeeImageProcessor(BaseImageProcessor):
    def __init__(self, is_train, randaug=True, input_size=224, interpolation='bicubic', **kwargs):
        super().__init__(**kwargs)
        self.is_train = is_train
        self.randaug = randaug
        self.input_size = input_size
        self.interpolation = interpolation
        self._transform = build_transform(is_train, randaug=randaug, input_size=input_size, interpolation=interpolation)

    def preprocess(self, images, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs) -> BatchFeature:
        images = make_list_of_images(images)
        images = [self._transform(image) for image in images]
        images = torch.tensor([image.numpy() for image in images])

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)

    def to_json_string(self) -> str:
        """
        Serializes this instance to a JSON string.

        Returns:
            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
        """
        dictionary = self.to_dict()

        for key, value in dictionary.items():
            if isinstance(value, np.ndarray):
                dictionary[key] = value.tolist()

        # make sure private name "_processor_class" is correctly
        # saved as "processor_class"
        _processor_class = dictionary.pop("_processor_class", None)
        if _processor_class is not None:
            dictionary["processor_class"] = _processor_class
        _transform = dictionary.pop("_transform", None)
        if _transform is not None:
            dictionary["_transform"] = str(type(_transform))

        return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"