def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
images = cv2.imread(path)
images = cv2.resize(images, (self.img_size[1], self.img_size[0]),
interpolation=cv2.INTER_CUBIC)
images = images / 255
return images
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\','/')
image = Image.open(path).convert('L')
image = image.rotate(45) #针对原始图像，旋转45度尽可能保留非0数据
x_data = numpy.array(image)
line, columns = numpy.nonzero(x_data) #选择非0区域
lmin = min(line) # 有非0像素的最小行
lmax = max(line)
colmin = min(columns)
colmax = max(columns)
image = Image.fromarray(x_data)
image = image.crop((colmin, lmin, colmax, lmax))
image = image.resize((224, 224)) #确定尺寸
# image = image.resize((32, 32))
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = x_data.reshape([224, 224, 1])
# x_data = x_data.reshape([32, 32, 1])
x_data = numpy.transpose(x_data, (2, 0, 1)) ## reshape
return x_data
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
img = image.load_img(path,
target_size=(origin_size[0], origin_size[1]))
x_data = image.img_to_array(img)
x_data = preprocess_input(x_data)
x_data = crop_image_center_80percent_to_input_function(x_data)
return x_data
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\', '/')
image = Image.open(path).convert('RGB')
return image
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = cv2.imread(path)
image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_CUBIC)
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
return x_data
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = Image.open(path).convert('L')
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
x_data = x_data.reshape(28, 28, 1)
return x_data
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = Image.open(path)
image = image.convert("RGB")
image = image.resize((64, 64), Image.BILINEAR)
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
return x_data
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
# path 为图片的真实路径
path = check_download(image_path, DATA_PATH)
image = cv2.imread(path)
image = np.array(image) # 图片转化为矩阵向量
image = transform.resize(image, output_shape=(80, 80))
return image
def input_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = cv2.imread(path)
image =cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
# image = cv2.cvtColor(image,cv2.IMREAD_GRAYSCALE)
image = cv2.resize(image, (224, 224) ,interpolation=cv2.INTER_CUBIC)
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
return x_data
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
# path 为图片的真实路径
path = check_download(image_path, DATA_PATH)
img = io.imread(path)
img = np.array(img) # 图片转化为矩阵向量
input_x = transform.resize(img, output_shape=(224, 224))
input_x = input_x.astype(np.float32)
return input_x
def input_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = cv2.imread(path)
image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_CUBIC)
x_data = numpy.array(image)
# print("1-", x_data.shape)
x_data = x_data.astype(numpy.float32)
# print("2-", x_data.shape)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
x_data = numpy.transpose(x_data, (2, 0, 1)) # reshape
# print("3-", x_data.shape)
return x_data
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
# path 为图片的真实路径
path = check_download(image_path, DATA_PATH)
image = cv2.imread(path)
image = cv2.resize(image, (200, 200), interpolation=cv2.INTER_CUBIC)
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0)
return x_data
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
image = Image.open(path).convert('RGB')
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
x_data = self.transform(image)
x_data = numpy.array(x_data)
return x_data
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\','/')
image = Image.open(path)
image = image.resize((128, 128))
x_data = numpy.array(image) #(width, height, channel)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data, 1.0 / 255.0) ## scale to [0,1] from [0,255]
x_data = numpy.transpose(x_data, (2, 0, 1)) ## reshape to (channel, width, height)
return x_data #(channel, width, height)
def output_x(self, image_path):
path = check_download(image_path, DATA_PATH)
# image = cv2.imread(path)
# image = cv2.resize(image, (224, 224))
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img = image.load_img(path, target_size=(img_size[0], img_size[1]))
x_data = image.img_to_array(img)
# x_data = numpy.expand_dims(x_data, axis=0)
x_data = preprocess_input(x_data)
# x_data = numpy.array(image)
# x_data = x_data.astype(numpy.float32)
# x_data = numpy.multiply(x_data, 1.0 / 255.0)
return x_data
def input_x(self, image_path):
path = check_download(image_path, DATA_PATH)
images = cv2.imread(path)
# 随机偏移
# rand_x = np.random.randint(0, 50, 1)[0]
# rand_y = np.random.randint(0, 50, 1)[0]
# M = np.float32([[1, 0, rand_x], [0, 1, rand_y]])
# images = cv2.warpAffine(images, M, (images.shape[1], images.shape[0]))
# self.img_augment[image_path] = [rand_x, rand_y]
images = cv2.resize(images, (self.img_size[1], self.img_size[0]),
interpolation=cv2.INTER_CUBIC)
images = images / 255
return images
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\', '/')
image = Image.open(path).convert('L')
image = image.crop((32, 32, 223, 223))
image = image.resize((128, 128))
x_data = numpy.array(image)
x_data = x_data.astype(numpy.float32)
x_data = x_data.reshape([128, 128, 1])
x_data = numpy.multiply(x_data,
1.0 / 255.0) ## scale to [0,1] from [0,255]
x_data = numpy.transpose(x_data, (2, 0, 1)) ## reshape
return x_data
def output_x(self, image_path):
'''
测试时使用，数据增强
'''
path = check_download(image_path, DATA_PATH)
x = Image.open(path).convert('RGB')
w,h=x.size
if h<512:
x = T.Resize((512,int(512 * (512 / h))))(x)
if w<512:
x=T.Resize((int(512*(512/w)),512))(x)
x=T.CenterCrop((448,448))(x)
#x=T.RandomCrop((448,448))(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(x)
return x
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH) #DATA_PATH=/data/train_code/tain63cd6642bd2d8d24de80cac9/data/input
x = Image.open(path).convert('RGB')
w,h=x.size
if h<512:
x = T.Resize((512,int(512 * (512 / h))))(x)
if w<512:
x=T.Resize((int(512*(512/w)),512))(x)
x=T.RandomCrop((448,448))(x)
x=T.RandomHorizontalFlip()(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(x)
return x
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\', '/')
img = image.load_img(path, target_size=img_size)
x = image.img_to_array(img)
# image = Image.open(path)
# image = image.resize(img_size)
# x_data = numpy.array(image)
# x_data = x_data.astype(numpy.float32)
# x_data = numpy.multiply(x_data, 1.0 / 255.0) ## scale to [0,1] from [0,255]
#使用Keras不需要转置
# x_data = numpy.transpose(x_data, (2, 0, 1)) ## reshape
return x
def input_x(self, image_path):
'''
参数为csv中作为输入x的一条数据，该方法会被Dataset多次调用
'''
path = check_download(image_path, DATA_PATH)
path = path.replace('\\', '/')
image = Image.open(path)
# image = image.resize((128, 128))
#image = image.resize((200, 200))
x_data = numpy.array(image)
#print("x_data.shape = ", x_data.shape)
x_data = x_data.astype(numpy.float32)
x_data = numpy.multiply(x_data,
1.0 / 255.0) ## scale to [0,1] from [0,255]
#print("x_data = ", x_data)
#x_data = numpy.transpose(x_data, (2, 0, 1)) ## reshape
#print("x_data = ", x_data.shape)
return x_data
