Exemplos de resize em Python

Exemplo n.º 1

Arquivo: train.py Projeto: for-aiur/torchcv-1

def transform_test(img, boxes, labels):
    img, boxes = resize(img, boxes, size=img_size, max_size=img_size)
    img = pad(img, (img_size, img_size))
    img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
    ])(img)
    boxes, labels = box_coder.encode(boxes, labels)
    return img, boxes, labels

Exemplo n.º 2

def transform_test(img, boxes, labels):
    img, boxes = resize(img, boxes, size=(img_w, img_h))
    img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
        # transforms.Normalize([0.5]*3,[0.5]*3)
    ])(img)
    boxes, labels = box_coder.encode(boxes, labels)
    return img, boxes, labels

Exemplo n.º 3

def transform_train_target(img, boxes, labels, img_size):
    if random.random() < 0.5:
        img, boxes = random_paste(img, boxes, max_ratio=4, fill=(123, 116, 103))
    img, boxes, labels = random_crop(img, boxes, labels)
    img, boxes = resize(img, boxes, size=(img_size, img_size), random_interpolation=True)
    img, boxes = random_flip(img, boxes)
    img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
    ])(img)
    return img

Exemplo n.º 4

Arquivo: train.py Projeto: duys3416/torchcv

def transform_train(img, boxes, labels):
    img = random_distort(img)
    if random.random() < 0.5:
        img, boxes = random_paste(img, boxes, max_ratio=4, fill=(123,116,103))
    img, boxes, labels = random_crop(img, boxes, labels)
    img, boxes = resize(img, boxes, size=(img_size,img_size), random_interpolation=True)
    img, boxes = random_flip(img, boxes)
    img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
    ])(img)
    # print ("labels: ")
    # print (labels.size())
    boxes, labels = box_coder.encode(boxes, labels)
    # print (labels.size())
    return img, boxes, labels

Exemplo n.º 5

Arquivo: train.py Projeto: zhaobinnku/dsod.pytorch

 def train_(img, boxes, labels):
     img = random_distort(img)
     if random.random() < 0.5:
         img, boxes = random_paste(img,
                                   boxes,
                                   max_ratio=4,
                                   fill=(123, 116, 103))
     img, boxes, labels = random_crop(img, boxes, labels)
     img, boxes = resize(img,
                         boxes,
                         size=(opt.img_size, opt.img_size),
                         random_interpolation=True)
     img, boxes = random_flip(img, boxes)
     img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
     boxes, labels = box_coder.encode(boxes, labels)
     return img, boxes, labels

Exemplo n.º 6

Arquivo: train.py Projeto: rebeen/Box-Attention-SSD-HOI

def transform_train(img, boxes, labels, switch):
    img = random_distort(img)
    if random.random() < 0.5:
        img, boxes = random_paste(img, boxes, max_ratio=4, fill=(123,116,103))
    img_, boxes_, labels_ = random_crop(img, boxes, labels)

    if switch == 1:
        while((0 not in labels) or (len(boxes_) == 1 and labels_[0]==0)) :
            img_, boxes_, labels_ = random_crop(img, boxes, labels)
    
    img, boxes, labels = img_, boxes_, labels_

    img, boxes = resize(img, boxes, size=(img_size,img_size), random_interpolation=True)
    img, boxes = random_flip(img, boxes)
    img = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
    ])(img)

    boxes_tmp = list(boxes.clone().data.cpu().numpy())
    labels_tmp = list(labels.clone().data.cpu().numpy())
   

    att_box = []

    if switch == 1:
       

        new_boxes = []
        new_labels = []

        for ii in range(len(labels_tmp)):
            if labels_tmp[ii] != 0:
                new_boxes.append(boxes_tmp[ii])
                new_labels.append(labels_tmp[ii])
            else:
                att_box.append(boxes_tmp[ii])

        boxes = torch.from_numpy(np.array(new_boxes))
        labels = torch.from_numpy(np.array(new_labels))    
    

    boxes, labels = box_coder.encode(boxes, labels)
    return img, boxes, labels, att_box #boxes_tmp, labels_tmp

Exemplo n.º 7

def transform_test(img, boxes, labels):
    img, boxes = resize(img, boxes, size=600)
    return img, boxes, labels

Exemplo n.º 8

def transform_train(img, boxes, labels):
    img, boxes = resize(img, boxes, size=600)
    img, boxes = random_flip(img, boxes)
    return img, boxes, labels

Exemplo n.º 9

Arquivo: test_dataloader.py Projeto: bigh2000/torchcv_edit

def transform(img, boxes, labels):
    img, boxes = resize(img, boxes, size=600)
    img, boxes = random_flip(img, boxes)
    return img, boxes, labels  # img is still PIL.Image

Exemplo n.º 10

Arquivo: test_dataloader.py Projeto: bigh2000/torchcv_edit

def transform(img, boxes, labels):
    img, boxes = resize(img, boxes, size=600)
    img, boxes = random_flip(img, boxes)
    img = transforms.ToTensor()(img)
    return img, boxes, labels

Exemplo n.º 11

Arquivo: train.py Projeto: zhaobinnku/dsod.pytorch

 def transform(img, boxes, labels):
     img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size))
     img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
     return img, boxes, labels

Exemplo n.º 12

Arquivo: train.py Projeto: zhaobinnku/dsod.pytorch

 def test_(img, boxes, labels):
     img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size))
     img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img)
     boxes, labels = box_coder.encode(boxes, labels)
     return img, boxes, labels