add readme (#10)
* Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * remove submodule * add mPLUG MiniGPT4 * Update Readme.md * Update Readme.md * Update Readme.md --------- Co-authored-by: Yuliang Liu <34134635+Yuliang-Liu@users.noreply.github.com>
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lz
345
models/mPLUG_Owl/pipeline/data_utils/randaugment.py
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345
models/mPLUG_Owl/pipeline/data_utils/randaugment.py
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import cv2
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import numpy as np
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from PIL import Image
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## aug functions
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def identity_func(img):
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return img
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def autocontrast_func(img, cutoff=0):
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'''
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same output as PIL.ImageOps.autocontrast
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'''
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n_bins = 256
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def tune_channel(ch):
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n = ch.size
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cut = cutoff * n // 100
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if cut == 0:
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high, low = ch.max(), ch.min()
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else:
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hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
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low = np.argwhere(np.cumsum(hist) > cut)
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low = 0 if low.shape[0] == 0 else low[0]
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high = np.argwhere(np.cumsum(hist[::-1]) > cut)
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high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
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if high <= low:
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table = np.arange(n_bins)
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else:
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scale = (n_bins - 1) / (high - low)
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offset = -low * scale
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table = np.arange(n_bins) * scale + offset
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table[table < 0] = 0
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table[table > n_bins - 1] = n_bins - 1
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table = table.clip(0, 255).astype(np.uint8)
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return table[ch]
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channels = [tune_channel(ch) for ch in cv2.split(img)]
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out = cv2.merge(channels)
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return out
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def equalize_func(img):
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'''
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same output as PIL.ImageOps.equalize
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PIL's implementation is different from cv2.equalize
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'''
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n_bins = 256
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def tune_channel(ch):
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hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
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non_zero_hist = hist[hist != 0].reshape(-1)
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step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
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if step == 0: return ch
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n = np.empty_like(hist)
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n[0] = step // 2
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n[1:] = hist[:-1]
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table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
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return table[ch]
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channels = [tune_channel(ch) for ch in cv2.split(img)]
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out = cv2.merge(channels)
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return out
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def rotate_func(img, degree, fill=(0, 0, 0)):
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'''
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like PIL, rotate by degree, not radians
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'''
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H, W = img.shape[0], img.shape[1]
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center = W / 2, H / 2
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M = cv2.getRotationMatrix2D(center, degree, 1)
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out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
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return out
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def solarize_func(img, thresh=128):
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'''
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same output as PIL.ImageOps.posterize
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'''
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table = np.array([el if el < thresh else 255 - el for el in range(256)])
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table = table.clip(0, 255).astype(np.uint8)
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out = table[img]
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return out
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def color_func(img, factor):
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'''
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same output as PIL.ImageEnhance.Color
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'''
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## implementation according to PIL definition, quite slow
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# degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
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# out = blend(degenerate, img, factor)
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# M = (
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# np.eye(3) * factor
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# + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
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# )[np.newaxis, np.newaxis, :]
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M = (
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np.float32([
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[0.886, -0.114, -0.114],
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[-0.587, 0.413, -0.587],
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[-0.299, -0.299, 0.701]]) * factor
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+ np.float32([[0.114], [0.587], [0.299]])
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)
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out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
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return out
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def contrast_func(img, factor):
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"""
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same output as PIL.ImageEnhance.Contrast
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"""
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mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
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table = np.array([(
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el - mean) * factor + mean
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for el in range(256)
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]).clip(0, 255).astype(np.uint8)
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out = table[img]
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return out
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def brightness_func(img, factor):
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'''
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same output as PIL.ImageEnhance.Contrast
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'''
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table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
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out = table[img]
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return out
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def sharpness_func(img, factor):
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'''
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The differences the this result and PIL are all on the 4 boundaries, the center
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areas are same
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'''
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kernel = np.ones((3, 3), dtype=np.float32)
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kernel[1][1] = 5
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kernel /= 13
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degenerate = cv2.filter2D(img, -1, kernel)
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if factor == 0.0:
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out = degenerate
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elif factor == 1.0:
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out = img
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else:
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out = img.astype(np.float32)
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degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
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out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
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out = out.astype(np.uint8)
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return out
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def shear_x_func(img, factor, fill=(0, 0, 0)):
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H, W = img.shape[0], img.shape[1]
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M = np.float32([[1, factor, 0], [0, 1, 0]])
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out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
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return out
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def translate_x_func(img, offset, fill=(0, 0, 0)):
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'''
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same output as PIL.Image.transform
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'''
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H, W = img.shape[0], img.shape[1]
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M = np.float32([[1, 0, -offset], [0, 1, 0]])
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out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
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return out
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def translate_y_func(img, offset, fill=(0, 0, 0)):
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'''
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same output as PIL.Image.transform
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'''
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H, W = img.shape[0], img.shape[1]
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M = np.float32([[1, 0, 0], [0, 1, -offset]])
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out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
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return out
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def posterize_func(img, bits):
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'''
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same output as PIL.ImageOps.posterize
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'''
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out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
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return out
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def shear_y_func(img, factor, fill=(0, 0, 0)):
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H, W = img.shape[0], img.shape[1]
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M = np.float32([[1, 0, 0], [factor, 1, 0]])
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out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
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return out
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def cutout_func(img, pad_size, replace=(0, 0, 0)):
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replace = np.array(replace, dtype=np.uint8)
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H, W = img.shape[0], img.shape[1]
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rh, rw = np.random.random(2)
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pad_size = pad_size // 2
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ch, cw = int(rh * H), int(rw * W)
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x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
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y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
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out = img.copy()
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out[x1:x2, y1:y2, :] = replace
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return out
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### level to args
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def enhance_level_to_args(MAX_LEVEL):
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def level_to_args(level):
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return ((level / MAX_LEVEL) * 1.8 + 0.1,)
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return level_to_args
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def shear_level_to_args(MAX_LEVEL, replace_value):
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def level_to_args(level):
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level = (level / MAX_LEVEL) * 0.3
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if np.random.random() > 0.5: level = -level
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return (level, replace_value)
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return level_to_args
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def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
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def level_to_args(level):
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level = (level / MAX_LEVEL) * float(translate_const)
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if np.random.random() > 0.5: level = -level
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return (level, replace_value)
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return level_to_args
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def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
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def level_to_args(level):
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level = int((level / MAX_LEVEL) * cutout_const)
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return (level, replace_value)
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return level_to_args
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def solarize_level_to_args(MAX_LEVEL):
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def level_to_args(level):
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level = int((level / MAX_LEVEL) * 256)
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return (level, )
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return level_to_args
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def none_level_to_args(level):
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return ()
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def posterize_level_to_args(MAX_LEVEL):
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def level_to_args(level):
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level = int((level / MAX_LEVEL) * 4)
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return (level, )
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return level_to_args
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def rotate_level_to_args(MAX_LEVEL, replace_value):
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def level_to_args(level):
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level = (level / MAX_LEVEL) * 30
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if np.random.random() < 0.5:
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level = -level
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return (level, replace_value)
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return level_to_args
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func_dict = {
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'Identity': identity_func,
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'AutoContrast': autocontrast_func,
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'Equalize': equalize_func,
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'Rotate': rotate_func,
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'Solarize': solarize_func,
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'Color': color_func,
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'Contrast': contrast_func,
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'Brightness': brightness_func,
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'Sharpness': sharpness_func,
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'ShearX': shear_x_func,
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'TranslateX': translate_x_func,
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'TranslateY': translate_y_func,
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'Posterize': posterize_func,
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'ShearY': shear_y_func,
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}
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translate_const = 10
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MAX_LEVEL = 10
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replace_value = (128, 128, 128)
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arg_dict = {
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'Identity': none_level_to_args,
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'AutoContrast': none_level_to_args,
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'Equalize': none_level_to_args,
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'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value),
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'Solarize': solarize_level_to_args(MAX_LEVEL),
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'Color': enhance_level_to_args(MAX_LEVEL),
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'Contrast': enhance_level_to_args(MAX_LEVEL),
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'Brightness': enhance_level_to_args(MAX_LEVEL),
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'Sharpness': enhance_level_to_args(MAX_LEVEL),
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'ShearX': shear_level_to_args(MAX_LEVEL, replace_value),
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'TranslateX': translate_level_to_args(
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translate_const, MAX_LEVEL, replace_value
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),
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'TranslateY': translate_level_to_args(
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translate_const, MAX_LEVEL, replace_value
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),
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'Posterize': posterize_level_to_args(MAX_LEVEL),
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'ShearY': shear_level_to_args(MAX_LEVEL, replace_value),
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}
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class RandomAugment(object):
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def __init__(self, N=2, M=10, isPIL=False, returnPIL=False, augs=[]):
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self.N = N
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self.M = M
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self.isPIL = isPIL
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self.returnPIL = returnPIL
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if augs:
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self.augs = augs
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else:
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self.augs = list(arg_dict.keys())
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def get_random_ops(self):
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sampled_ops = np.random.choice(self.augs, self.N)
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return [(op, 0.5, self.M) for op in sampled_ops]
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def __call__(self, img):
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if self.isPIL:
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img = np.array(img)
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ops = self.get_random_ops()
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for name, prob, level in ops:
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if np.random.random() > prob:
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continue
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args = arg_dict[name](level)
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img = func_dict[name](img, *args)
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if self.returnPIL:
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img = img.astype('uint8')
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img = Image.fromarray(img)
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return img
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if __name__ == '__main__':
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a = RandomAugment()
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img = np.random.randn(32, 32, 3)
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a(img)
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