目标：利用CV2通过对图像旋转，实现图像扩增
功能1：图像旋转
函数： 构建旋转矩阵：M=cv2.getRotationMatrix2D（(w // 2, h // 2),-90, 1）
第一个参数为旋转中心，第二个为旋转角度(负数：顺时针旋转，正数：逆时针旋转)，第三个为旋转后的缩放因子
img=cv2.warpAffine(img,M,(2*cols,2*rows)) # 第三个参数是输出图像的尺寸中心
参考文档 ： https://cloud.tencent.com/developer/article/1347695
旋转90度目标框坐标
| 原目标框：左上角（w0,h0） 右下角（w1,h1） | 图像旋转后：(w_0=H-h0-h,h_0=w0),(w_1=H-h1+h,h_1=w1) |
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代码功能：
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def img_rot(image_path,annotation_path,image_save_path,annotation_save_path):
list_img = os.listdir(image_path)
path_img = [os.path.join(image_path, path) for path in list_img]
num_i = 20000000
for path in path_img:
num_i += 1
txt = os.path.basename(path).replace('.jpg', '') # 返回文件名
txt_path = txt + '.txt'
annot_path = os.path.join(annotation_path, txt_path)
with open(annot_path, encoding='utf-8') as fp:
text = fp.read()
data = text.split(' ')
if len(data) == 6:
img = cv2.imread(path)
img_h, img_w, _ = img.shape
# cv2.flip(img, 1)
# 图像旋转
if img_w > img_h:
padding = (img_w - img_h) // 2
center = (img_w // 2, img_w // 2)
img_padded = np.zeros(shape=(img_w, img_w, 3), dtype=np.uint8)
img_padded[padding:padding + img_h, :, :] = img
M = cv2.getRotationMatrix2D(center, -90, 1)
rotated_padded = cv2.warpAffine(img_padded, M, (img_w, img_w))
img = rotated_padded[:, padding:padding + img_h, :]
elif img_w < img_h:
padding = (img_h - img_w) // 2
center = (img_h // 2, img_h // 2)
# plt_1 = (img_h - 546 - 116, 422)
# plt_2 = (img_h - 662 + 116, 496)
img_padded = np.zeros(shape=(img_h, img_h, 3), dtype=np.uint8)
# img_padded[padding:padding + h, :, :] = img
img_padded[:, padding:padding + img_w, :] = img
M = cv2.getRotationMatrix2D(center, -90, 1)
rotated_padded = cv2.warpAffine(img_padded, M, (img_h, img_h))
img = rotated_padded[padding:padding + img_w, :, :]
img_name = 'core_battery' + str(num_i) + '.jpg'
cv2.imwrite(os.path.join(image_save_path, img_name), img)
# w_0,h_0,w_1,h_1=int(data[2]),int(data[3]),int(data[4]),int(data[5])
w_0, h_0, w_1, h_1 = int(data[2]), int(data[3]), int(data[4]), int(data[5])
annot_w = w_1 - w_0
annot_h = h_1 - h_0
w_0_0 = img_h - h_0 - annot_h
h_0_0 = w_0
w_1_0 = img_h - h_1 + annot_h
h_1_0 = w_1
string = data[0] + ' ' + data[1] + ' ' + str(w_0_0) + ' ' + str(h_0_0) + ' ' + str(w_1_0) + ' ' + str(h_1_0)
txt_name = 'core_battery' + str(num_i) + '.txt'
with open(os.path.join(annotation_save_path, txt_name), 'w') as fp:
fp.write(string)
if __name__ == '__main__':
image_save_path = 'C:\data\core_500\image_new' #处理后的图像保存位置
annotation_save_path = 'C:\data\core_500/annotation_new' #处理后的目标框保存位置
annotation_path = 'C:\data\core_500\Annotation' #原图像保存位置
image_path = 'C:\data\core_500\Image' #原目标框保存位置
# 图像翻转
img_flip(image_path,annotation_path,image_save_path,annotation_save_path)
# 图像旋转
img_rot(image_path, annotation_path, image_save_path, annotation_save_path)