Create utils/augmentations.py (#3877)
* Create `utils/augmentations.py` * cleanup
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# YOLOv5 image augmentation functions
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import random
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import cv2
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import math
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import numpy as np
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from utils.general import segment2box, resample_segments
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from utils.metrics import bbox_ioa
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def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
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# HSV color-space augmentation
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if hgain or sgain or vgain:
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r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
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dtype = im.dtype # uint8
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
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def hist_equalize(im, clahe=True, bgr=False):
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# Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
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yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
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if clahe:
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c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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yuv[:, :, 0] = c.apply(yuv[:, :, 0])
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else:
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yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
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return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
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def replicate(im, labels):
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# Replicate labels
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h, w = im.shape[:2]
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boxes = labels[:, 1:].astype(int)
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x1, y1, x2, y2 = boxes.T
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s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
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for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
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x1b, y1b, x2b, y2b = boxes[i]
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bh, bw = y2b - y1b, x2b - x1b
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yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
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x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
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im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
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labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
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return im, labels
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better test mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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if auto: # minimum rectangle
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
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elif scaleFill: # stretch
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dw, dh = 0.0, 0.0
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new_unpad = (new_shape[1], new_shape[0])
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ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, ratio, (dw, dh)
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def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
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border=(0, 0)):
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
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# targets = [cls, xyxy]
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height = im.shape[0] + border[0] * 2 # shape(h,w,c)
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width = im.shape[1] + border[1] * 2
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# Center
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C = np.eye(3)
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C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
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C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
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# Perspective
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P = np.eye(3)
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P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
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P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
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# Rotation and Scale
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R = np.eye(3)
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a = random.uniform(-degrees, degrees)
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# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
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s = random.uniform(1 - scale, 1 + scale)
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# s = 2 ** random.uniform(-scale, scale)
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
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# Shear
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S = np.eye(3)
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S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
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# Translation
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T = np.eye(3)
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T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
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T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
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# Combined rotation matrix
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M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
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if perspective:
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im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
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else: # affine
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im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
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# Visualize
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# import matplotlib.pyplot as plt
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# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
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# ax[0].imshow(img[:, :, ::-1]) # base
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# ax[1].imshow(img2[:, :, ::-1]) # warped
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# Transform label coordinates
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n = len(targets)
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if n:
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use_segments = any(x.any() for x in segments)
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new = np.zeros((n, 4))
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if use_segments: # warp segments
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segments = resample_segments(segments) # upsample
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for i, segment in enumerate(segments):
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xy = np.ones((len(segment), 3))
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xy[:, :2] = segment
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xy = xy @ M.T # transform
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xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
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# clip
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new[i] = segment2box(xy, width, height)
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else: # warp boxes
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xy = np.ones((n * 4, 3))
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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# clip
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new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
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new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
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# filter candidates
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
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targets = targets[i]
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targets[:, 1:5] = new[i]
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return im, targets
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def copy_paste(im, labels, segments, probability=0.5):
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# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
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n = len(segments)
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if probability and n:
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h, w, c = im.shape # height, width, channels
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im_new = np.zeros(im.shape, np.uint8)
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for j in random.sample(range(n), k=round(probability * n)):
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l, s = labels[j], segments[j]
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box = w - l[3], l[2], w - l[1], l[4]
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ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
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if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
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labels = np.concatenate((labels, [[l[0], *box]]), 0)
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segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
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cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
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result = cv2.bitwise_and(src1=im, src2=im_new)
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result = cv2.flip(result, 1) # augment segments (flip left-right)
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i = result > 0 # pixels to replace
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# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
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im[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug
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return im, labels, segments
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def cutout(im, labels):
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# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
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h, w = im.shape[:2]
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# create random masks
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scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
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for s in scales:
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mask_h = random.randint(1, int(h * s))
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mask_w = random.randint(1, int(w * s))
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# box
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xmin = max(0, random.randint(0, w) - mask_w // 2)
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ymin = max(0, random.randint(0, h) - mask_h // 2)
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xmax = min(w, xmin + mask_w)
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ymax = min(h, ymin + mask_h)
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# apply random color mask
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im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
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# return unobscured labels
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if len(labels) and s > 0.03:
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box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
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ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
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labels = labels[ioa < 0.60] # remove >60% obscured labels
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return labels
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def mixup(im, labels, im2, labels2):
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# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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im = (im * r + im2 * (1 - r)).astype(np.uint8)
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labels = np.concatenate((labels, labels2), 0)
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return im, labels
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def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
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# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
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w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
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w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
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ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
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+6
-235
@@ -1,4 +1,4 @@
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# Dataset utils and dataloaders
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# YOLOv5 dataset utils and dataloaders
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import glob
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import hashlib
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@@ -14,7 +14,6 @@ from pathlib import Path
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from threading import Thread
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import cv2
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import math
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import numpy as np
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import torch
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import torch.nn.functional as F
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@@ -23,9 +22,9 @@ from PIL import Image, ExifTags
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from utils.augmentations import augment_hsv, copy_paste, letterbox, mixup, random_perspective
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from utils.general import check_requirements, check_file, check_dataset, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, \
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xyn2xy, segment2box, segments2boxes, resample_segments, clean_str
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from utils.metrics import bbox_ioa
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xyn2xy, segments2boxes, clean_str
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from utils.torch_utils import torch_distributed_zero_first
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# Parameters
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@@ -523,12 +522,10 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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img, labels = load_mosaic(self, index)
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shapes = None
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# MixUp https://arxiv.org/pdf/1710.09412.pdf
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# MixUp augmentation
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if random.random() < hyp['mixup']:
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img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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img = (img * r + img2 * (1 - r)).astype(np.uint8)
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labels = np.concatenate((labels, labels2), 0)
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img, labels = mixup(img, labels, *load_mosaic(self, random.randint(0, self.n - 1)))
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else:
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# Load image
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@@ -639,32 +636,6 @@ def load_image(self, index):
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return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
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def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
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if hgain or sgain or vgain:
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r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
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dtype = img.dtype # uint8
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
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def hist_equalize(img, clahe=True, bgr=False):
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# Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
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yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
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if clahe:
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c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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yuv[:, :, 0] = c.apply(yuv[:, :, 0])
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else:
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yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
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return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
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def load_mosaic(self, index):
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# loads images in a 4-mosaic
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@@ -796,205 +767,6 @@ def load_mosaic9(self, index):
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return img9, labels9
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def replicate(img, labels):
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# Replicate labels
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h, w = img.shape[:2]
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boxes = labels[:, 1:].astype(int)
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x1, y1, x2, y2 = boxes.T
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s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
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for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
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x1b, y1b, x2b, y2b = boxes[i]
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bh, bw = y2b - y1b, x2b - x1b
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yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
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x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
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img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
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labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
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return img, labels
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def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
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# Resize and pad image while meeting stride-multiple constraints
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shape = img.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better test mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
|
||||
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
|
||||
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
|
||||
if auto: # minimum rectangle
|
||||
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
|
||||
elif scaleFill: # stretch
|
||||
dw, dh = 0.0, 0.0
|
||||
new_unpad = (new_shape[1], new_shape[0])
|
||||
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
|
||||
|
||||
dw /= 2 # divide padding into 2 sides
|
||||
dh /= 2
|
||||
|
||||
if shape[::-1] != new_unpad: # resize
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
|
||||
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
|
||||
return img, ratio, (dw, dh)
|
||||
|
||||
|
||||
def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
|
||||
border=(0, 0)):
|
||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
||||
# targets = [cls, xyxy]
|
||||
|
||||
height = img.shape[0] + border[0] * 2 # shape(h,w,c)
|
||||
width = img.shape[1] + border[1] * 2
|
||||
|
||||
# Center
|
||||
C = np.eye(3)
|
||||
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
|
||||
C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
|
||||
|
||||
# Perspective
|
||||
P = np.eye(3)
|
||||
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
|
||||
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
|
||||
|
||||
# Rotation and Scale
|
||||
R = np.eye(3)
|
||||
a = random.uniform(-degrees, degrees)
|
||||
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
|
||||
s = random.uniform(1 - scale, 1 + scale)
|
||||
# s = 2 ** random.uniform(-scale, scale)
|
||||
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
|
||||
|
||||
# Shear
|
||||
S = np.eye(3)
|
||||
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
|
||||
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
|
||||
|
||||
# Translation
|
||||
T = np.eye(3)
|
||||
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
|
||||
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
|
||||
|
||||
# Combined rotation matrix
|
||||
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
|
||||
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
|
||||
if perspective:
|
||||
img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
|
||||
else: # affine
|
||||
img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
|
||||
|
||||
# Visualize
|
||||
# import matplotlib.pyplot as plt
|
||||
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
|
||||
# ax[0].imshow(img[:, :, ::-1]) # base
|
||||
# ax[1].imshow(img2[:, :, ::-1]) # warped
|
||||
|
||||
# Transform label coordinates
|
||||
n = len(targets)
|
||||
if n:
|
||||
use_segments = any(x.any() for x in segments)
|
||||
new = np.zeros((n, 4))
|
||||
if use_segments: # warp segments
|
||||
segments = resample_segments(segments) # upsample
|
||||
for i, segment in enumerate(segments):
|
||||
xy = np.ones((len(segment), 3))
|
||||
xy[:, :2] = segment
|
||||
xy = xy @ M.T # transform
|
||||
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
|
||||
|
||||
# clip
|
||||
new[i] = segment2box(xy, width, height)
|
||||
|
||||
else: # warp boxes
|
||||
xy = np.ones((n * 4, 3))
|
||||
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
||||
xy = xy @ M.T # transform
|
||||
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
|
||||
|
||||
# create new boxes
|
||||
x = xy[:, [0, 2, 4, 6]]
|
||||
y = xy[:, [1, 3, 5, 7]]
|
||||
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||||
|
||||
# clip
|
||||
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
|
||||
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
|
||||
|
||||
# filter candidates
|
||||
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
|
||||
targets = targets[i]
|
||||
targets[:, 1:5] = new[i]
|
||||
|
||||
return img, targets
|
||||
|
||||
|
||||
def copy_paste(img, labels, segments, probability=0.5):
|
||||
# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
|
||||
n = len(segments)
|
||||
if probability and n:
|
||||
h, w, c = img.shape # height, width, channels
|
||||
im_new = np.zeros(img.shape, np.uint8)
|
||||
for j in random.sample(range(n), k=round(probability * n)):
|
||||
l, s = labels[j], segments[j]
|
||||
box = w - l[3], l[2], w - l[1], l[4]
|
||||
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
|
||||
if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
|
||||
labels = np.concatenate((labels, [[l[0], *box]]), 0)
|
||||
segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
|
||||
cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
|
||||
|
||||
result = cv2.bitwise_and(src1=img, src2=im_new)
|
||||
result = cv2.flip(result, 1) # augment segments (flip left-right)
|
||||
i = result > 0 # pixels to replace
|
||||
# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
|
||||
img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug
|
||||
|
||||
return img, labels, segments
|
||||
|
||||
|
||||
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
|
||||
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
|
||||
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
|
||||
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
|
||||
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
|
||||
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
|
||||
|
||||
|
||||
def cutout(image, labels):
|
||||
# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
|
||||
h, w = image.shape[:2]
|
||||
|
||||
# create random masks
|
||||
scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
|
||||
for s in scales:
|
||||
mask_h = random.randint(1, int(h * s))
|
||||
mask_w = random.randint(1, int(w * s))
|
||||
|
||||
# box
|
||||
xmin = max(0, random.randint(0, w) - mask_w // 2)
|
||||
ymin = max(0, random.randint(0, h) - mask_h // 2)
|
||||
xmax = min(w, xmin + mask_w)
|
||||
ymax = min(h, ymin + mask_h)
|
||||
|
||||
# apply random color mask
|
||||
image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
|
||||
|
||||
# return unobscured labels
|
||||
if len(labels) and s > 0.03:
|
||||
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
|
||||
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
|
||||
labels = labels[ioa < 0.60] # remove >60% obscured labels
|
||||
|
||||
return labels
|
||||
|
||||
|
||||
def create_folder(path='./new'):
|
||||
# Create folder
|
||||
if os.path.exists(path):
|
||||
@@ -1012,7 +784,6 @@ def flatten_recursive(path='../datasets/coco128'):
|
||||
|
||||
def extract_boxes(path='../datasets/coco128'): # from utils.datasets import *; extract_boxes()
|
||||
# Convert detection dataset into classification dataset, with one directory per class
|
||||
|
||||
path = Path(path) # images dir
|
||||
shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing
|
||||
files = list(path.rglob('*.*'))
|
||||
|
||||
Reference in new issue
Block a user