子產品介紹
資料讀取部分通常包含如下功能：
- 建立資料類，友善管理。
- 類的功能1，解析資料文本，擷取樣本名字等。
- 類的功能2，資料增強。
- 類的功能3，資料實時處理，比如讀取圖像，标準化等。
- 建立2個隊列，訓練集隊列和驗證集隊列。
代碼架構
第一步：建立資料讀取類（DataLoader），類的初始化函數通常用于解析訓練資料的文本，擷取其中的檔案名。class DataLoader: def __init__(self, file, mode): self.input = cfg.train.input_size self.root_path = cfg.train.root_path # read text file: save train name list self.name_list = [] data = open(file, 'r') for line in data: line = line.strip() self.name_list.append(line) random.shuffle(self.name_list) # if mode == 'train': # self.name_list = self.name_list[0:150] # data1 = open('../B7Data/1025_color_new/split_1.txt', 'r') # for line1 in data1: # line1 = line1.strip() # self.name_list.append(line1) # random.shuffle(self.name_list) # print('混合的訓練集數量: ', len(self.name_list))
第二步：資料增強，圖像的基本處理方法，包含顔色增強系列，随機翻轉，随機旋轉等。def image_enhance(self, img): p = random.randint(1, 3) a1 = random.uniform(0.8, 2) a2 = random.uniform(0.8, 1.4) a3 = random.uniform(0.8, 1.7) a4 = random.uniform(0.8, 2.5) img = Image.fromarray(img) img = ImageEnhance.Color(img).enhance(a1) if p == 0 else img img = ImageEnhance.Brightness(img).enhance(a2) if p == 1 else img img = ImageEnhance.Contrast(img).enhance(a3) if p == 2 else img img = ImageEnhance.Sharpness(img).enhance(a4) if p == 3 else img img = np.array(img) return img def flip_img(self, img): flipped = (np.random.random() <= 0.5) if flipped: img = img[:, ::-1, :] return img @staticmethod def show_image(name, data): cv2.imshow(name, data) cv2.waitKey(0) cv2.destroyAllWindows() def pose_rotation(self, img): w, h, c = img.shape deg = random.uniform(-15.0, 15.0) M_rotate = affine_rotation_matrix(angle=deg) transform_matrix = transform_matrix_offset_center(M_rotate, x=w, y=h) img_result = affine_transform_cv2(img, transform_matrix) return img_result
第三步：資料的實時處理，比如讀取圖像，資料标準化，然後将資料以一個batch塊的方式放入隊列。def load_data(self, batch_size, queue, thread, name_queue, mode): """ 從名字隊列中逐個讀取訓練資料，按一個batch存儲 :param batch_size: 批次大小 :param queue: 存儲資料的隊列 :param thread: 配置設定的線程數 :param name_queue: 訓練資料集的名字隊列 :param mode: train or valid mode, 對應訓練集和驗證集上不同的處理方式 :return: 存有資料的queue """ image = [] label = [] data_name = [] thread_name = [] sign_0 = 0 sign_1 = 0 while 1: data = name_queue.get() # print('data: ', data) d1 = data.split(' ') # 讀取資料：解析資料集名字和标簽 if len(d1) == 2: data_image = self.root_path + d1[0] # print("data path: ", data_image) if float(d1[-1]) >= 10: data_label = 1 sign_1 = sign_1 + 1 # elif float(d1[-1]) >= 10: # continue else: data_label = 0 sign_0 = sign_0 + 1 else: ss = ' '.join(d1[:-1]) data_image = self.root_path + ss if float(d1[-1]) >= 10: data_label = 1 sign_1 = sign_1 + 1 # elif float(d1[-1]) >= 10: # continue else: data_label = 0 sign_0 = sign_0 + 1 if not os.path.exists(data_image): print('資料不存在：', data_image) img = cv2.imread(data_image) # self.show_image('ori image', img) # 資料增強 if mode == 'train': # self.show_image('ori image', img) # img = self.image_enhance(img) # self.show_image('enhance', img) img = self.flip_img(img) # self.show_image('flip', img) # img = self.pose_rotation(img) # img = cv2.resize(img, (self.input[1], self.input[0])) # self.show_image('resize', img) img = img.astype(np.float32) # "../B7Data/1025_color_new/train_1025_color.txt" 計算如下的均值和标準差 std_bgr = [6.550119402970968, 6.312448303275082, 8.977662213055952] mean_bgr = [33.65559903, 120.61937841, 116.81338165] # img = (img - np.mean(img, axis=(0, 1))) / (np.std(img, axis=(0, 1)) + 1e-8) # img = (img - np.array([33.65559903, 120.61937841, 116.81338165]))/np.array(std_bgr) img = img/255.0 # img = np.reshape(img, (self.input[0], self.input[1], 3)) # print(img) if mode == 'train-l': if (sign_0 <= 16) and (float(d1[-1]) < 10): data_name.append(data_image) image.append(img) label.append(data_label) if (sign_1 <= 16) and (float(d1[-1]) >= 10): data_name.append(data_image) image.append(img) label.append(data_label) else: data_name.append(data_image) image.append(img) thread_name.append(thread) label.append(data_label) if len(image) != batch_size: continue queue.put([data_name, thread_name, np.array(image), np.array(label)]) # print('名字: ', data_name) # print('線程: ', thread_name) image = [] label = [] data_name = [] thread_name = []
第四步：建立2個隊列，分别動态的讀取訓練資料和驗證集資料def train_set_queue(): """ 讀取訓練集資料訓練,主要分為三個部分,如下所示： 1.讀取訓練集名字：從文本中讀取所有訓練集名字,存入清單. 2.建立隊列,動态讀取訓練集名字至隊列：配置設定單獨的線程, 讀取所有訓練集名字至train_name_queue. 3.建立訓練集隊列, 讀取資料；建立多個線程, 同時從train_name_queue中擷取名字,并根據名字從硬碟讀資料至train_queue. :return: 存儲訓練集資料的隊列 """ train_file = cfg.train.train_set human_data_train = DataLoader(train_file, 'train') print("num of train data: ", len(human_data_train.name_list)) # 單線程讀取訓練集名字 train_name_queue = Queue(cfg.train.train_num) name_process = Process(target=human_data_train.name_queue_, args=(train_name_queue, )) name_process.start() # create queue and read train data cache_train_data = 100 train_thread_num = 4 train_queue = Queue(cache_train_data) for thread in range(train_thread_num): p_train = Process(target=human_data_train.load_data, args=(cfg.train.batch_size, train_queue, thread, train_name_queue, 'train')) p_train.start() return train_queue def valid_set_queue(): """ 處理流程與train_set_queue()函數一樣. :return: 存儲驗證集的名字. """ valid_file = cfg.train.valid_set human_data_valid = DataLoader(valid_file, 'valid') print("num of valid data: ", len(human_data_valid.name_list)) # 單獨隊列，讀取驗證集名字 valid_name_queue = Queue(cfg.train.valid_num) valid_name_process = Process(target=human_data_valid.name_queue_, args=(valid_name_queue,)) valid_name_process.start() # create queue and read valid data cache_valid_data = 32 valid_thread_num = 2 valid_queue = Queue(cache_valid_data) for thread in range(valid_thread_num): p_valid = Process(target=human_data_valid.load_data, args=(cfg.train.batch_size, valid_queue, thread, valid_name_queue, 'valid')) p_valid.start() return valid_queue ```
完整代碼
下面的代碼是
DataLoader.py
import random
from PIL import Image, ImageEnhance
from cv_rotation import *
from config import cfg
from multiprocessing import Queue, Process
import os
class DataLoader:
def __init__(self, file, mode):
self.input = cfg.train.input_size
self.root_path = cfg.train.root_path
# read text file: save train name list
self.name_list = []
data = open(file, 'r')
for line in data:
line = line.strip()
self.name_list.append(line)
random.shuffle(self.name_list)
# if mode == 'train':
# self.name_list = self.name_list[0:150]
# data1 = open('../B7Data/1025_color_new/split_1.txt', 'r')
# for line1 in data1:
# line1 = line1.strip()
# self.name_list.append(line1)
# random.shuffle(self.name_list)
# print('混合的訓練集數量: ', len(self.name_list))
def name_queue_(self, name_queue):
count = 0
random.shuffle(self.name_list)
while True:
if count >= len(self.name_list):
count = 0
random.shuffle(self.name_list)
continue
name_queue.put(self.name_list[count])
# print(self.name_list[count])
count = count + 1
# if name_queue.full():
# print('隊列滿')
# print('count: ', count)
def image_enhance(self, img):
p = random.randint(1, 3)
a1 = random.uniform(0.8, 2)
a2 = random.uniform(0.8, 1.4)
a3 = random.uniform(0.8, 1.7)
a4 = random.uniform(0.8, 2.5)
img = Image.fromarray(img)
img = ImageEnhance.Color(img).enhance(a1) if p == 0 else img
img = ImageEnhance.Brightness(img).enhance(a2) if p == 1 else img
img = ImageEnhance.Contrast(img).enhance(a3) if p == 2 else img
img = ImageEnhance.Sharpness(img).enhance(a4) if p == 3 else img
img = np.array(img)
return img
def flip_img(self, img):
flipped = (np.random.random() <= 0.5)
if flipped:
img = img[:, ::-1, :]
return img
@staticmethod
def show_image(name, data):
cv2.imshow(name, data)
cv2.waitKey(0)
cv2.destroyAllWindows()
def pose_rotation(self, img):
w, h, c = img.shape
deg = random.uniform(-15.0, 15.0)
M_rotate = affine_rotation_matrix(angle=deg)
transform_matrix = transform_matrix_offset_center(M_rotate, x=w, y=h)
img_result = affine_transform_cv2(img, transform_matrix)
return img_result
def load_data(self, batch_size, queue, thread, name_queue, mode):
"""
從名字隊列中逐個讀取訓練資料，按一個batch存儲
:param batch_size: 批次大小
:param queue: 存儲資料的隊列
:param thread: 配置設定的線程數
:param name_queue: 訓練資料集的名字隊列
:param mode: train or valid mode, 對應訓練集和驗證集上不同的處理方式
:return: 存有資料的queue
"""
image = []
label = []
data_name = []
thread_name = []
sign_0 = 0
sign_1 = 0
while 1:
data = name_queue.get()
# print('data: ', data)
d1 = data.split(' ')
# 讀取資料：解析資料集名字和标簽
if len(d1) == 2:
data_image = self.root_path + d1[0]
# print("data path: ", data_image)
if float(d1[-1]) >= 10:
data_label = 1
sign_1 = sign_1 + 1
# elif float(d1[-1]) >= 10:
# continue
else:
data_label = 0
sign_0 = sign_0 + 1
else:
ss = ' '.join(d1[:-1])
data_image = self.root_path + ss
if float(d1[-1]) >= 10:
data_label = 1
sign_1 = sign_1 + 1
# elif float(d1[-1]) >= 10:
# continue
else:
data_label = 0
sign_0 = sign_0 + 1
if not os.path.exists(data_image):
print('資料不存在：', data_image)
img = cv2.imread(data_image)
# self.show_image('ori image', img)
# 資料增強
if mode == 'train':
# self.show_image('ori image', img)
# img = self.image_enhance(img)
# self.show_image('enhance', img)
img = self.flip_img(img)
# self.show_image('flip', img)
# img = self.pose_rotation(img)
# img = cv2.resize(img, (self.input[1], self.input[0]))
# self.show_image('resize', img)
img = img.astype(np.float32)
# "../B7Data/1025_color_new/train_1025_color.txt" 計算如下的均值和标準差
std_bgr = [6.550119402970968, 6.312448303275082, 8.977662213055952]
mean_bgr = [33.65559903, 120.61937841, 116.81338165]
# img = (img - np.mean(img, axis=(0, 1))) / (np.std(img, axis=(0, 1)) + 1e-8)
# img = (img - np.array([33.65559903, 120.61937841, 116.81338165]))/np.array(std_bgr)
img = img/255.0
# img = np.reshape(img, (self.input[0], self.input[1], 3))
# print(img)
if mode == 'train-l':
if (sign_0 <= 16) and (float(d1[-1]) < 10):
data_name.append(data_image)
image.append(img)
label.append(data_label)
if (sign_1 <= 16) and (float(d1[-1]) >= 10):
data_name.append(data_image)
image.append(img)
label.append(data_label)
else:
data_name.append(data_image)
image.append(img)
thread_name.append(thread)
label.append(data_label)
if len(image) != batch_size:
continue
queue.put([data_name, thread_name, np.array(image), np.array(label)])
# print('名字: ', data_name)
# print('線程: ', thread_name)
image = []
label = []
data_name = []
thread_name = []
def train_set_queue():
"""
讀取訓練集資料訓練,主要分為三個部分,如下所示：
1.讀取訓練集名字：從文本中讀取所有訓練集名字,存入清單.
2.建立隊列,動态讀取訓練集名字至隊列：配置設定單獨的線程, 讀取所有訓練集名字至train_name_queue.
3.建立訓練集隊列, 讀取資料；建立多個線程, 同時從train_name_queue中擷取名字,并根據名字從硬碟讀資料至train_queue.
:return: 存儲訓練集資料的隊列
"""
train_file = cfg.train.train_set
human_data_train = DataLoader(train_file, 'train')
print("num of train data: ", len(human_data_train.name_list))
# 單線程讀取訓練集名字
train_name_queue = Queue(cfg.train.train_num)
name_process = Process(target=human_data_train.name_queue_, args=(train_name_queue, ))
name_process.start()
# create queue and read train data
cache_train_data = 100
train_thread_num = 4
train_queue = Queue(cache_train_data)
for thread in range(train_thread_num):
p_train = Process(target=human_data_train.load_data,
args=(cfg.train.batch_size, train_queue, thread, train_name_queue, 'train'))
p_train.start()
return train_queue
def valid_set_queue():
"""
處理流程與train_set_queue()函數一樣.
:return: 存儲驗證集的名字.
"""
valid_file = cfg.train.valid_set
human_data_valid = DataLoader(valid_file, 'valid')
print("num of valid data: ", len(human_data_valid.name_list))
# 單獨隊列，讀取驗證集名字
valid_name_queue = Queue(cfg.train.valid_num)
valid_name_process = Process(target=human_data_valid.name_queue_, args=(valid_name_queue,))
valid_name_process.start()
# create queue and read valid data
cache_valid_data = 32
valid_thread_num = 2
valid_queue = Queue(cache_valid_data)
for thread in range(valid_thread_num):
p_valid = Process(target=human_data_valid.load_data,
args=(cfg.train.batch_size, valid_queue, thread, valid_name_queue, 'valid'))
p_valid.start()
return valid_queue
上述代碼中，在資料增強的随機旋轉中，需要從
cv_rotation.py
中擷取相應的實作函數，完整代碼如下：
import numpy as np
import cv2
# import scipy.ndimage as ndi
def affine_rotation_matrix(angle=(-20, 20)):
"""Create an affine transform matrix for image rotation.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
angle : int/float or tuple of two int/float
Degree to rotate, usually -180 ~ 180.
- int/float, a fixed angle.
- tuple of 2 floats/ints, randomly sample a value as the angle between these 2 values.
Returns
-------
numpy.array
An affine transform matrix.
"""
if isinstance(angle, tuple):
theta = np.pi / 180 * np.random.uniform(angle[0], angle[1])
else:
theta = np.pi / 180 * angle
rotation_matrix = np.array([[np.cos(theta), np.sin(theta), 0],
[-np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
return rotation_matrix
def affine_horizontal_flip_matrix(prob=0.5):
"""Create an affine transformation matrix for image horizontal flipping.
NOTE: In OpenCV, x is width and y is height.
Parameters
----------
prob : float
Probability to flip the image. 1.0 means always flip.
Returns
-------
numpy.array
An affine transform matrix.
"""
factor = np.random.uniform(0, 1)
if prob >= factor:
filp_matrix = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
return filp_matrix
else:
filp_matrix = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
return filp_matrix
def affine_shift_matrix(wrg=(-0.1, 0.1), hrg=(-0.1, 0.1), w=200, h=200):
"""Create an affine transform matrix for image shifting.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
wrg : float or tuple of floats
Range to shift on width axis, -1 ~ 1.
- float, a fixed distance.
- tuple of 2 floats, randomly sample a value as the distance between these 2 values.
hrg : float or tuple of floats
Range to shift on height axis, -1 ~ 1.
- float, a fixed distance.
- tuple of 2 floats, randomly sample a value as the distance between these 2 values.
w, h : int
The width and height of the image.
Returns
-------
numpy.array
An affine transform matrix.
"""
if isinstance(wrg, tuple):
tx = np.random.uniform(wrg[0], wrg[1]) * w
else:
tx = wrg * w
if isinstance(hrg, tuple):
ty = np.random.uniform(hrg[0], hrg[1]) * h
else:
ty = hrg * h
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
return shift_matrix
def affine_shear_matrix(x_shear=(-0.1, 0.1), y_shear=(-0.1, 0.1)):
"""Create affine transform matrix for image shearing.
NOTE: In OpenCV, x is width and y is height.
Parameters
-----------
shear : tuple of two floats
Percentage of shears for width and height directions.
Returns
-------
numpy.array
An affine transform matrix.
"""
# if len(shear) != 2:
# raise AssertionError(
# "shear should be tuple of 2 floats, or you want to use tl.prepro.shear rather than tl.prepro.shear2 ?"
# )
# if isinstance(shear, tuple):
# shear = list(shear)
# if is_random:
# shear[0] = np.random.uniform(-shear[0], shear[0])
# shear[1] = np.random.uniform(-shear[1], shear[1])
if isinstance(x_shear, tuple):
x_shear = np.random.uniform(x_shear[0], x_shear[1])
if isinstance(y_shear, tuple):
y_shear = np.random.uniform(y_shear[0], y_shear[1])
shear_matrix = np.array([[1, x_shear, 0],
[y_shear, 1, 0],
[0, 0, 1]])
return shear_matrix
def affine_zoom_matrix(zoom_range=(0.8, 1.1)):
"""Create an affine transform matrix for zooming/scaling an image's height and width.
OpenCV format, x is width.
Parameters
-----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
zoom_range : float or tuple of 2 floats
The zooming/scaling ratio, greater than 1 means larger.
- float, a fixed ratio.
- tuple of 2 floats, randomly sample a value as the ratio between these 2 values.
Returns
-------
numpy.array
An affine transform matrix.
"""
if isinstance(zoom_range, (float, int)):
scale = zoom_range
elif isinstance(zoom_range, tuple):
scale = np.random.uniform(zoom_range[0], zoom_range[1])
else:
raise Exception("zoom_range: float or tuple of 2 floats")
zoom_matrix = np.array([[scale, 0, 0],
[0, scale, 0],
[0, 0, 1]])
return zoom_matrix
def affine_respective_zoom_matrix(w_range=0.8, h_range=1.1):
"""Get affine transform matrix for zooming/scaling that height and width are changed independently.
OpenCV format, x is width.
Parameters
-----------
w_range : float or tuple of 2 floats
The zooming/scaling ratio of width, greater than 1 means larger.
- float, a fixed ratio.
- tuple of 2 floats, randomly sample a value as the ratio between 2 values.
h_range : float or tuple of 2 floats
The zooming/scaling ratio of height, greater than 1 means larger.
- float, a fixed ratio.
- tuple of 2 floats, randomly sample a value as the ratio between 2 values.
Returns
-------
numpy.array
An affine transform matrix.
"""
if isinstance(h_range, (float, int)):
zy = h_range
elif isinstance(h_range, tuple):
zy = np.random.uniform(h_range[0], h_range[1])
else:
raise Exception("h_range: float or tuple of 2 floats")
if isinstance(w_range, (float, int)):
zx = w_range
elif isinstance(w_range, tuple):
zx = np.random.uniform(w_range[0], w_range[1])
else:
raise Exception("w_range: float or tuple of 2 floats")
zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]])
return zoom_matrix
# affine transform
def transform_matrix_offset_center(matrix, x, y):
"""Convert the matrix from Cartesian coordinates (the origin in the middle of image)
to Image coordinates (the origin on the top-left of image).
Parameters
----------
matrix : numpy.array Transform matrix.
x and y : 2 int, Size of image.
Returns
-------
numpy.array
The transform matrix.
Examples
--------
- See ``tl.prepro.rotation``, ``tl.prepro.shear``, ``tl.prepro.zoom``.
"""
o_x = (x - 1) / 2.0
o_y = (y - 1) / 2.0
offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])
reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])
transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)
return transform_matrix
# def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):
# """Return transformed images by given an affine matrix in Scipy format (x is height).
#
# Parameters
# ----------
# x : numpy.array
# An image with dimension of [row, col, channel] (default).
# transform_matrix : numpy.array
# Transform matrix (offset center), can be generated by ``transform_matrix_offset_center``
# channel_index : int
# Index of channel, default 2.
# fill_mode : str
# Method to fill missing pixel, default `nearest`, more options `constant`, `reflect` or `wrap`, see `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`__
# cval : float
# Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0
# order : int
# The order of interpolation. The order has to be in the range 0-5:
# - 0 Nearest-neighbor
# - 1 Bi-linear (default)
# - 2 Bi-quadratic
# - 3 Bi-cubic
# - 4 Bi-quartic
# - 5 Bi-quintic
# - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`__
#
# Returns
# -------
# numpy.array
# A processed image.
#
# Examples
# --------
# >>> M_shear = tl.prepro.affine_shear_matrix(intensity=0.2, is_random=False)
# >>> M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=0.8)
# >>> M_combined = M_shear.dot(M_zoom)
# >>> transform_matrix = tl.prepro.transform_matrix_offset_center(M_combined, h, w)
# >>> result = tl.prepro.affine_transform(image, transform_matrix)
#
# """
# # transform_matrix = transform_matrix_offset_center()
# # asdihasid
# # asd
#
# x = np.rollaxis(x, channel_index, 0)
# final_affine_matrix = transform_matrix[:2, :2]
# final_offset = transform_matrix[:2, 2]
# channel_images = [
# ndi.interpolation.
# affine_transform(x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval)
# for x_channel in x
# ]
# x = np.stack(channel_images, axis=0)
# x = np.rollaxis(x, 0, channel_index + 1)
# return x
#
#
# apply_transform = affine_transform
def affine_transform_cv2(x, transform_matrix, flags=None, border_mode='constant'):
"""Return transformed images by given an affine matrix in OpenCV format (x is width).
(Powered by OpenCV2, faster than ``tl.prepro.affine_transform``)
Parameters
----------
x : numpy.array
An image with dimension of [row, col, channel] (default).
transform_matrix : numpy.array
A transform matrix, OpenCV format.
border_mode : str
- `constant`, pad the image with a constant value (i.e. black or 0)
- `replicate`, the row or column at the very edge of the original is replicated to the extra border.
Examples
--------
# >>> M_shear = tl.prepro.affine_shear_matrix(intensity=0.2, is_random=False)
# >>> M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=0.8)
# >>> M_combined = M_shear.dot(M_zoom)
# >>> result = tl.prepro.affine_transform_cv2(image, M_combined)
"""
rows, cols = x.shape[0], x.shape[1]
if flags is None:
flags = cv2.INTER_AREA
if border_mode is 'constant':
border_mode = cv2.BORDER_CONSTANT
elif border_mode is 'replicate':
border_mode = cv2.BORDER_REPLICATE
else:
raise Exception("unsupport border_mode, check cv.BORDER_ for more details.")
return cv2.warpAffine(x, transform_matrix[0:2, :], (cols, rows), flags=flags, borderMode=border_mode)
def affine_transform_keypoints(coords_list, transform_matrix):
"""Transform keypoint coordinates according to a given affine transform matrix.
OpenCV format, x is width.
Note that, for pose estimation task, flipping requires maintaining the left and right body information.
We should not flip the left and right body, so please use ``tl.prepro.keypoint_random_flip``.
Parameters
-----------
coords_list : list of list of tuple/list
The coordinates
e.g., the keypoint coordinates of every person in an image.
transform_matrix : numpy.array
Transform matrix, OpenCV format.
Examples
---------
# >>> # 1. get all affine transform matrices
# >>> M_rotate = tl.prepro.affine_rotation_matrix(angle=20)
# >>> M_flip = tl.prepro.affine_horizontal_flip_matrix(prob=1)
# >>> # 2. combine all affine transform matrices to one matrix
# >>> M_combined = dot(M_flip).dot(M_rotate)
# >>> # 3. transfrom the matrix from Cartesian coordinate (the origin in the middle of image)
# >>> # to Image coordinate (the origin on the top-left of image)
# >>> transform_matrix = tl.prepro.transform_matrix_offset_center(M_combined, x=w, y=h)
# >>> # 4. then we can transfrom the image once for all transformations
# >>> result = tl.prepro.affine_transform_cv2(image, transform_matrix) # 76 times faster
# >>> # 5. transform keypoint coordinates
# >>> coords = [[(50, 100), (100, 100), (100, 50), (200, 200)], [(250, 50), (200, 50), (200, 100)]]
# >>> coords_result = tl.prepro.affine_transform_keypoints(coords, transform_matrix)
"""
coords_result_list = []
for coords in coords_list:
# print('=====================')
# print(coords)
coords = np.asarray(coords)
coords = coords.transpose([1, 0])
coords = np.insert(coords, 2, 1, axis=0)
coords_result = np.matmul(transform_matrix, coords)
coords_result = coords_result[0:2, :].transpose([1, 0])
coords_result_list.append(coords_result)
return coords_result_list
if __name__ == '__main__':
import os
render_path = '../train_0628/render/'
label_path = '../train_0628/label/'
r = os.listdir(render_path)
for j in range(len(r)):
src = cv2.imread(render_path + r[j])
h, w, c = src.shape
label = np.loadtxt(label_path + r[j][:-4] + '.txt')
coords = label[:, 1:3]
for i in range(7):
cv2.circle(src, (int(coords[i][0]), int(coords[i][1])), 5, (255, 0, 0), -1)
cv2.imshow('src', src)
cv2.waitKey(0)
# 1. get all affine transform matrices
M_rotate = affine_rotation_matrix(angle=19)
# 2. transform the matrix from Cartesian coordinate (the origin in the middle of image)
# to Image coordinate (the origin on the top-left of image)
transform_matrix = transform_matrix_offset_center(M_rotate, x=w, y=h)
# 3. then we can transform the image once for all transformations
dst = affine_transform_cv2(src, transform_matrix) # 76 times faster
# cv2.imshow('rotate', dst)
# cv2.waitKey(0)
# 4. transform keypoint coordinates
print(coords)
coords = [[(coords[0, 0], coords[0, 1]),
(coords[1, 0], coords[1, 1]),
(coords[2, 0], coords[2, 1]),
(coords[3, 0], coords[3, 1]),
(coords[4, 0], coords[4, 1]),
(coords[5, 0], coords[5, 1]),
(coords[6, 0], coords[6, 1])]]
coords_result = affine_transform_keypoints(coords, transform_matrix)
print(coords_result)
dst_color = dst
dst_points_lt = []
for i in range(7):
dst_points_lt = (int(coords_result[0][i, 0]), int(coords_result[0][i, 1]))
cv2.circle(dst_color, dst_points_lt, 3, (0, 0, 255), -1)
cv2.imshow('result', dst_color)
cv2.waitKey()
cv2.destroyAllWindows()
# coords_x = coords_result[0][:,0]
# coords_y = coords_result[0][:,1]
# x_min = coords_x[np.argmin(coords_x)]
# x_max = coords_x[np.argmax(coords_x)]
# y_min = coords_y[np.argmin(coords_y)]
# y_max = coords_y[np.argmax(coords_y)]
# cv2.rectangle(dst_color,(int(x_min),int(y_min)), (int(x_max),int(y_max)), (255,0,0))
# cv2.imwrite("rotation_result.jpg", dst_color)
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