Python cv2.Rect方法代碼示例

本文整理匯總了Python中cv2.Rect方法的典型用法代碼示例。如果您正苦於以下問題：Python cv2.Rect方法的具體用法？Python cv2.Rect怎麽用？Python cv2.Rect使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cv2的用法示例。

在下文中一共展示了cv2.Rect方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: getitem

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def __getitem__(self, index):
        datafiles = self.files[index]
        image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
        label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
        size = image.shape
        name = datafiles["name"]
        if self.scale:
            image, label = self.generate_scale_label(image, label)
        image = np.asarray(image, np.float32)
        image -= self.mean
        img_h, img_w = label.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT, 
                value=(0.0, 0.0, 0.0))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
        else:
            img_pad, label_pad = image, label

        img_h, img_w = label_pad.shape
        h_off = random.randint(0, img_h - self.crop_h)
        w_off = random.randint(0, img_w - self.crop_w)
        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);
        image = np.asarray(img_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)
        label = np.asarray(label_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)
        #image = image[:, :, ::-1]  # change to BGR
        image = image.transpose((2, 0, 1))
        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label = label[:, ::flip]

        return image.copy(), label.copy(), np.array(size), name

開發者ID:speedinghzl，項目名稱:pytorch-segmentation-toolbox，代碼行數:39，代碼來源:datasets.py

示例2: getitem

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def __getitem__(self, index):
        datafiles = self.files[index]
        image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
        label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
        size = image.shape
        name = datafiles["name"]
        if self.scale:
            scale = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
            f_scale = scale[random.randint(0, 5)]
            # f_scale = 0.5 + random.randint(0, 15) / 10.0  # random resize between 0.5 and 2
            image = cv2.resize(image, None, fx=f_scale, fy=f_scale, interpolation=cv2.INTER_LINEAR)
            label = cv2.resize(label, None, fx=f_scale, fy=f_scale, interpolation=cv2.INTER_NEAREST)

        image = np.asarray(image, np.float32)

        image -= self.mean
        # image = image.astype(np.float32) / 255.0
        image = image[:, :, ::-1]  # change to RGB
        img_h, img_w = label.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0,
                                         pad_w, cv2.BORDER_CONSTANT,
                                         value=(0.0, 0.0, 0.0))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0,
                                           pad_w, cv2.BORDER_CONSTANT,
                                           value=(self.ignore_label,))
        else:
            img_pad, label_pad = image, label

        img_h, img_w = label_pad.shape
        h_off = random.randint(0, img_h - self.crop_h)
        w_off = random.randint(0, img_w - self.crop_w)
        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);
        image = np.asarray(img_pad[h_off: h_off + self.crop_h, w_off: w_off + self.crop_w], np.float32)
        label = np.asarray(label_pad[h_off: h_off + self.crop_h, w_off: w_off + self.crop_w], np.float32)

        image = image.transpose((2, 0, 1))  # NHWC -> NCHW

        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label = label[:, ::flip]

        return image.copy(), label.copy(), np.array(size), name

開發者ID:Reagan1311，項目名稱:DABNet，代碼行數:48，代碼來源:cityscapes.py

示例3: getitem

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def __getitem__(self, index):
        datafiles = self.files[index]
        image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
        label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
        size = image.shape
        name = datafiles["name"]
        if self.scale:
            scale = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0]  # random resize between 0.5 and 2
            f_scale = scale[random.randint(0, 5)]
            # f_scale = 0.5 + random.randint(0, 15) / 10.0  #random resize between 0.5 and 2
            image = cv2.resize(image, None, fx=f_scale, fy=f_scale, interpolation=cv2.INTER_LINEAR)
            label = cv2.resize(label, None, fx=f_scale, fy=f_scale, interpolation=cv2.INTER_NEAREST)

        image = np.asarray(image, np.float32)

        image -= self.mean
        # image = image.astype(np.float32) / 255.0
        image = image[:, :, ::-1]  # change to RGB
        img_h, img_w = label.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0,
                                         pad_w, cv2.BORDER_CONSTANT,
                                         value=(0.0, 0.0, 0.0))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0,
                                           pad_w, cv2.BORDER_CONSTANT,
                                           value=(self.ignore_label,))
        else:
            img_pad, label_pad = image, label

        img_h, img_w = label_pad.shape
        h_off = random.randint(0, img_h - self.crop_h)
        w_off = random.randint(0, img_w - self.crop_w)
        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);
        image = np.asarray(img_pad[h_off: h_off + self.crop_h, w_off: w_off + self.crop_w], np.float32)
        label = np.asarray(label_pad[h_off: h_off + self.crop_h, w_off: w_off + self.crop_w], np.float32)

        image = image.transpose((2, 0, 1))  # NHWC -> NCHW

        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label = label[:, ::flip]

        return image.copy(), label.copy(), np.array(size), name

開發者ID:Reagan1311，項目名稱:DABNet，代碼行數:48，代碼來源:camvid.py

示例4: getitem

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def __getitem__(self, index):
        datafiles = self.files[index]
        image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR)
        label = cv2.imread(datafiles["label"], cv2.IMREAD_GRAYSCALE)
        size = image.shape
        name = datafiles["name"]
        if self.scale:
            f_scale = 0.5 + random.randint(0, 15) / 10.0  #random resize between 0.5 and 2 
            image = cv2.resize(image, None, fx=f_scale, fy=f_scale, interpolation = cv2.INTER_LINEAR)
            label = cv2.resize(label, None, fx=f_scale, fy=f_scale, interpolation = cv2.INTER_NEAREST)

        image = np.asarray(image, np.float32)
        
        image = image[:, :, ::-1]  # change to BGR
        image -= self.mean
        img_h, img_w = label.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT, 
                value=(0.0, 0.0, 0.0))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
        else:
            img_pad, label_pad = image, label

        img_h, img_w = label_pad.shape
        h_off = random.randint(0, img_h - self.crop_h)
        w_off = random.randint(0, img_w - self.crop_w)
        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);
        image = np.asarray(img_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)
        label = np.asarray(label_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)
        
        
        image = image.transpose((2, 0, 1)) # NHWC -> NCHW
        
        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label = label[:, ::flip]


        return image.copy(), label.copy(), np.array(size), name

開發者ID:wutianyiRosun，項目名稱:CGNet，代碼行數:47，代碼來源:cityscapes.py

示例5: preprocess

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def preprocess(self, image, label, annotation):
        """
        preprocess() emulate the pre-processing occuring in the vgg16 caffe
        prototxt.
        """
        # image = cv2.convertTo(image, cv2.CV_64F)
        image, label_1s, label_2s, label_3s, label = self.generate_scale_label(image, label, annotation)
        image = np.asarray(image, np.float32)
        image -= self.mean
        image *= self.scale
        
        img_h, img_w = label_1s.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT, 
                value=(0.0, 0.0, 0.0))
            label_1s_pad = cv2.copyMakeBorder(label_1s, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
            label_2s_pad = cv2.copyMakeBorder(label_2s, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
            label_3s_pad = cv2.copyMakeBorder(label_3s, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
        else:
            img_pad, label_1s_pad, label_2s_pad, label_3s_pad, label_pad = image, label_1s, label_2s, label_3s, label

        img_h, img_w = label_1s_pad.shape
        if self.phase == 'Train':
            h_off = random.randint(0, img_h - self.crop_h)
            w_off = random.randint(0, img_w - self.crop_w)
        else:
            h_off = (img_h - self.crop_h) / 2
            w_off = (img_w - self.crop_w) / 2

        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);

        image = np.asarray(img_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w].copy(), np.float32)
        label_1s = np.asarray(label_1s_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w].copy(), np.float32)
        label_2s = np.asarray(label_2s_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w].copy(), np.float32)
        label_3s = np.asarray(label_3s_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w].copy(), np.float32)
        label    = np.asarray(label_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w].copy(), np.float32)

        #image = image[:, :, ::-1]  # change to BGR
        image = image.transpose((2, 0, 1))

        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label_1s = label_1s[:, ::flip]
            label_2s = label_2s[:, ::flip]
            label_3s = label_3s[:, ::flip]
            label    = label[:, ::flip]

        return image, label_1s, label_2s, label_3s, label

開發者ID:speedinghzl，項目名稱:Scale-Adaptive-Network，代碼行數:63，代碼來源:multilayerbbox_v2.py

示例6: preprocess

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import Rect [as 別名]
def preprocess(self, image, label):
        """
        preprocess() emulate the pre-processing occuring in the vgg16 caffe
        prototxt.
        """
        # image = cv2.convertTo(image, cv2.CV_64F)
        image, label = self.generate_scale_label(image, label)
        image = np.asarray(image, np.float32)
        image -= self.mean
        image *= self.scale

        img_h, img_w = label.shape
        pad_h = max(self.crop_h - img_h, 0)
        pad_w = max(self.crop_w - img_w, 0)
        if pad_h > 0 or pad_w > 0:
            img_pad = cv2.copyMakeBorder(image, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT, 
                value=(0.0, 0.0, 0.0))
            label_pad = cv2.copyMakeBorder(label, 0, pad_h, 0, 
                pad_w, cv2.BORDER_CONSTANT,
                value=(self.ignore_label,))
        else:
            img_pad, label_pad = image, label

        img_h, img_w = label_pad.shape
        if self.phase == 'Train':
            h_off = random.randint(0, img_h - self.crop_h)
            w_off = random.randint(0, img_w - self.crop_w)
        else:
            h_off = (img_h - self.crop_h) / 2
            w_off = (img_w - self.crop_w) / 2

        # roi = cv2.Rect(w_off, h_off, self.crop_w, self.crop_h);

        image = np.asarray(img_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)
        label = np.asarray(label_pad[h_off : h_off+self.crop_h, w_off : w_off+self.crop_w], np.float32)

        #image = image[:, :, ::-1]  # change to BGR
        image = image.transpose((2, 0, 1))

        if self.is_mirror:
            flip = np.random.choice(2) * 2 - 1
            image = image[:, :, ::flip]
            label = label[:, ::flip]

        return image, label

開發者ID:speedinghzl，項目名稱:Scale-Adaptive-Network，代碼行數:48，代碼來源:layer.py

注：本文中的cv2.Rect方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。

示例1: __getitem__

示例2: __getitem__