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image.py
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image.py
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#!/usr/bin/python
# encoding: utf-8
import random
import os
import pdb
import numpy as np
from PIL import Image
from PIL import ImageFile
from cfg import cfg
ImageFile.LOAD_TRUNCATED_IMAGES = True
def scale_image_channel(im, c, v):
cs = list(im.split())
cs[c] = cs[c].point(lambda i: i * v)
out = Image.merge(im.mode, tuple(cs))
return out
def distort_image(im, hue, sat, val):
im = im.convert('HSV')
cs = list(im.split())
cs[1] = cs[1].point(lambda i: i * sat)
cs[2] = cs[2].point(lambda i: i * val)
def change_hue(x):
x += hue * 255
if x > 255:
x -= 255
if x < 0:
x += 255
return x
cs[0] = cs[0].point(change_hue)
im = Image.merge(im.mode, tuple(cs))
im = im.convert('RGB')
# constrain_image(im)
return im
def rand_scale(s):
scale = random.uniform(1, s)
if (random.randint(1, 10000) % 2):
return scale
return 1. / scale
def random_distort_image(im, hue, saturation, exposure):
dhue = random.uniform(-hue, hue)
dsat = rand_scale(saturation)
dexp = rand_scale(exposure)
res = distort_image(im, dhue, dsat, dexp)
return res
def data_augmentation(img, shape, jitter, hue, saturation, exposure, flag=True):
oh = img.height
ow = img.width
dw = int(ow * jitter)
dh = int(oh * jitter)
if flag:
pleft = random.randint(-dw, dw)
pright = random.randint(-dw, dw)
ptop = random.randint(-dh, dh)
pbot = random.randint(-dh, dh)
flip = random.randint(1, 10000) % 2
swidth = ow - pleft - pright
sheight = oh - ptop - pbot
sx = float(swidth) / ow
sy = float(sheight) / oh
cropped = img.crop((pleft, ptop, pleft + swidth - 1, ptop + sheight - 1))
dx = (float(pleft) / ow) / sx
dy = (float(ptop) / oh) / sy
sized = cropped.resize(shape)
if flip:
sized = sized.transpose(Image.FLIP_LEFT_RIGHT)
img = random_distort_image(sized, hue, saturation, exposure)
else:
# pleft, pright, ptop, pbot, flip = 0, 0, 0, 0, 0
flip, dx, dy, sx, sy = 0, 0, 0, 1, 1
img = img.resize(shape)
return img, flip, dx, dy, sx, sy
def data_augmentation_detect(img, label, shape, jitter, hue, saturation, exposure, flag=True):
oh = img.height
ow = img.width
label = np.reshape(label, (-1, 5))
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label[:, [1, 3]] = ow - label[:, [3, 1]]
if random.random() < 0.5:
max_bbox = np.concatenate([np.min(label[:, 1:3], axis=0), np.max(label[:, 3:5], axis=0)], axis=-1)
max_l_trans = max_bbox[0]
max_u_trans = max_bbox[1]
max_r_trans = ow - max_bbox[2]
max_d_trans = oh - max_bbox[3]
crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans)))
crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans)))
crop_xmax = min(ow, int(max_bbox[2] + random.uniform(0, max_r_trans)))
crop_ymax = min(oh, int(max_bbox[3] + random.uniform(0, max_d_trans)))
img = img.crop((crop_xmin, crop_ymin, crop_xmax, crop_ymax))
label[:, [0, 2]] = label[:, [0, 2]] - crop_xmin
label[:, [1, 3]] = label[:, [1, 3]] - crop_ymin
ow = img.width
oh = img.height
def fill_truth_detection(labpath, w, h, flip, dx, dy, sx, sy):
max_boxes = cfg.max_boxes
label = np.zeros((max_boxes, 5))
if os.path.exists(labpath) and os.path.getsize(labpath):
bs = np.loadtxt(labpath)
if bs is None:
return label
bs = np.reshape(bs, (-1, 5))
cc = 0
for i in range(bs.shape[0]):
# Filter out bboxes not in base classes
imgid = labpath.split('/')[-1].split('.')[0]
clsid = int(bs[i][0])
# if clsid not in cfg.base_ids:
# continue
if clsid in cfg.base_ids:
keepit = True
elif cfg.yolo_joint and imgid in cfg.metaids:
keepit = True
else:
keepit = False
if not keepit:
continue
x1 = bs[i][1] - bs[i][3] / 2
y1 = bs[i][2] - bs[i][4] / 2
x2 = bs[i][1] + bs[i][3] / 2
y2 = bs[i][2] + bs[i][4] / 2
x1 = min(0.999, max(0, x1 * sx - dx))
y1 = min(0.999, max(0, y1 * sy - dy))
x2 = min(0.999, max(0, x2 * sx - dx))
y2 = min(0.999, max(0, y2 * sy - dy))
bs[i][1] = (x1 + x2) / 2
bs[i][2] = (y1 + y2) / 2
bs[i][3] = (x2 - x1)
bs[i][4] = (y2 - y1)
if flip:
bs[i][1] = 0.999 - bs[i][1]
if bs[i][3] < 0.001 or bs[i][4] < 0.001:
continue
label[cc] = bs[i]
cc += 1
if cc >= 50:
break
label = np.reshape(label, (-1))
return label
def fill_truth_detection_meta(labpath, w, h, flip, dx, dy, sx, sy):
max_boxes = cfg.max_boxes
n_cls = len(cfg.base_classes)
label = np.zeros((n_cls, max_boxes, 5))
if os.path.getsize(labpath):
bs = np.loadtxt(labpath)
if bs is None:
return label
bs = np.reshape(bs, (-1, 5))
ccs = [0] * n_cls
for i in range(bs.shape[0]):
# Filter out bboxes not in base classes
clsid = int(bs[i][0])
if clsid not in cfg.base_ids:
continue
x1 = bs[i][1] - bs[i][3] / 2
y1 = bs[i][2] - bs[i][4] / 2
x2 = bs[i][1] + bs[i][3] / 2
y2 = bs[i][2] + bs[i][4] / 2
x1 = min(0.999, max(0, x1 * sx - dx))
y1 = min(0.999, max(0, y1 * sy - dy))
x2 = min(0.999, max(0, x2 * sx - dx))
y2 = min(0.999, max(0, y2 * sy - dy))
bs[i][1] = (x1 + x2) / 2
bs[i][2] = (y1 + y2) / 2
bs[i][3] = (x2 - x1)
bs[i][4] = (y2 - y1)
if flip:
bs[i][1] = 0.999 - bs[i][1]
if bs[i][3] < 0.001 or bs[i][4] < 0.001:
continue
# Copy bbox info for building target
ind = cfg.base_ids.index(clsid)
if ind >= n_cls or ccs[ind] >= cfg.max_boxes:
pdb.set_trace()
label[ind][ccs[ind]] = bs[i]
label[ind][ccs[ind]][0] = ind
ccs[ind] += 1
if sum(ccs) >= 50:
break
label = np.reshape(label, (n_cls, -1))
return label
def fill_truth_detection_metaV2(labpath):
max_boxes = cfg.max_boxes
n_cls = len(cfg.base_classes)
label = np.zeros((n_cls, max_boxes, 5))
if os.path.exists(labpath) and os.path.getsize(labpath):
bs = np.loadtxt(labpath)
if bs is None:
return label
bs = np.reshape(bs, (-1, 5))
ccs = [0] * n_cls
for i in range(bs.shape[0]):
# Filter out bboxes not in base classes
clsid = int(bs[i][0])
if clsid not in cfg.base_ids:
continue
x1 = bs[i][1] - bs[i][3] / 2
y1 = bs[i][2] - bs[i][4] / 2
x2 = bs[i][1] + bs[i][3] / 2
y2 = bs[i][2] + bs[i][4] / 2
bs[i][1] = x1
bs[i][2] = y1
bs[i][3] = x2
bs[i][4] = y2
ind = cfg.base_ids.index(clsid)
if ind >= n_cls or ccs[ind] >= cfg.max_boxes:
pdb.set_trace()
label[ind][ccs[ind]] = bs[i]
label[ind][ccs[ind]][0] = ind
ccs[ind] += 1
if sum(ccs) >= 50:
break
return label
def load_label(labpath, w, h, flip, dx, dy, sx, sy):
label = []
# if os.path.exists(labpath) and os.path.getsize(labpath):
if os.path.getsize(labpath):
bs = np.loadtxt(labpath)
if bs is None:
return label
bs = np.reshape(bs, (-1, 5))
cc = 0
for i in range(bs.shape[0]):
x1 = bs[i][1] - bs[i][3] / 2
y1 = bs[i][2] - bs[i][4] / 2
x2 = bs[i][1] + bs[i][3] / 2
y2 = bs[i][2] + bs[i][4] / 2
x1 = min(0.999, max(0, x1 * sx - dx))
y1 = min(0.999, max(0, y1 * sy - dy))
x2 = min(0.999, max(0, x2 * sx - dx))
y2 = min(0.999, max(0, y2 * sy - dy))
bs[i][1] = (x1 + x2) / 2
bs[i][2] = (y1 + y2) / 2
bs[i][3] = (x2 - x1)
bs[i][4] = (y2 - y1)
if flip:
bs[i][1] = 0.999 - bs[i][1]
if bs[i][3] < 0.001 or bs[i][4] < 0.001:
continue
# label[cc] = bs[i]
label.append(bs[i, 1:])
cc += 1
if cc >= 50:
break
return label
def load_labelV2(labpath):
label = []
if os.path.getsize(labpath):
bs = np.loadtxt(labpath)
if bs is None:
return label
bs = np.reshape(bs, (-1, 5))
cc = 0
for i in range(bs.shape[0]):
x1 = bs[i][1] - bs[i][3] / 2
y1 = bs[i][2] - bs[i][4] / 2
x2 = bs[i][1] + bs[i][3] / 2
y2 = bs[i][2] + bs[i][4] / 2
bs[i][1] = x1
bs[i][2] = y1
bs[i][3] = x2
bs[i][4] = y2
label.append(bs[i, 1:])
cc += 1
if cc >= 50:
break
return label
def load_data_detection(imgpath, labpath, shape, jitter, hue, saturation, exposure, data_aug=True):
# labpath = imgpath.replace('images', 'labels').replace('JPEGImages', 'labels').replace('.jpg', '.txt').replace('.png','.txt')
# labpath = imgpath.replace('images', 'labels_1c/aeroplane').replace('JPEGImages', 'labels_1c/aeroplane').replace('.jpg', '.txt').replace('.png','.txt')
## data augmentation
img = Image.open(imgpath).convert('RGB')
img, flip, dx, dy, sx, sy = data_augmentation(img, shape, jitter, hue, saturation, exposure, flag=data_aug)
if cfg.metayolo:
label = fill_truth_detection_meta(labpath, img.width, img.height, flip, dx, dy, 1. / sx, 1. / sy)
else:
label = fill_truth_detection(labpath, img.width, img.height, flip, dx, dy, 1. / sx, 1. / sy)
return img, label
def load_data_detectionV2(imgpath, labpath, shape, jitter, hue, saturation, exposure, data_aug=True):
img = Image.open(imgpath).convert('RGB')
label = fill_truth_detection_metaV2(labpath)
def load_data_with_label(imgpath, labpath, shape, jitter, hue, saturation, exposure, data_aug=True):
## data augmentation
img = Image.open(imgpath).convert('RGB')
img, flip, dx, dy, sx, sy = data_augmentation(img, shape, jitter, hue, saturation, exposure, flag=data_aug)
label = load_label(labpath, img.width, img.height, flip, dx, dy, 1. / sx, 1. / sy)
return img, label