Python cv2_util.findContours方法代码示例

# 需要导入模块: from maskrcnn_benchmark.utils import cv2_util [as 别名]
# 或者: from maskrcnn_benchmark.utils.cv2_util import findContours [as 别名]
def overlay_mask(self, image, predictions):
        """
        Adds the instances contours for each predicted object.
        Each label has a different color.

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask` and `labels`.
        """
        masks = predictions.get_field("mask").numpy()
        labels = predictions.get_field("labels")

        colors = self.compute_colors_for_labels(labels).tolist()

        for mask, color in zip(masks, colors):
            thresh = mask[0, :, :, None]
            contours, hierarchy = cv2_util.findContours(
                thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
            )
            image = cv2.drawContours(image, contours, -1, color, 3)

        composite = image

        return composite 

# 需要导入模块: from maskrcnn_benchmark.utils import cv2_util [as 别名]
# 或者: from maskrcnn_benchmark.utils.cv2_util import findContours [as 别名]
def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

# 需要导入模块: from maskrcnn_benchmark.utils import cv2_util [as 别名]
# 或者: from maskrcnn_benchmark.utils.cv2_util import findContours [as 别名]
def overlay_mask(self, image, predictions):
        """
        Adds the instances contours for each predicted object.
        Each label has a different color.

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask` and `labels`.
        """
        masks = predictions.get_field("mask").numpy()
        labels = predictions.get_field("labels")

        colors = self.compute_colors_for_labels(labels).tolist()

        for mask, color in zip(masks, colors):
            thresh = mask[0, :, :, None].astype(np.uint8)
            contours, hierarchy = cv2_util.findContours(
                thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
            )
            image = cv2.drawContours(image, contours, -1, color, 3)

        composite = image

        return composite 

# 需要导入模块: from maskrcnn_benchmark.utils import cv2_util [as 别名]
# 或者: from maskrcnn_benchmark.utils.cv2_util import findContours [as 别名]
def overlay_mask(self, image, predictions):
        """
        Adds the instances contours for each predicted object.
        Each label has a different color.

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask` and `labels`.
        """
        masks = predictions.get_field("mask").numpy()
        labels = predictions.get_field("labels")
        colors = self.compute_colors_for_labels(labels)

        image = image.astype(np.float)
        masks = masks.squeeze(1)
        for mask, color in zip(masks, colors):
            idx = np.nonzero(mask)
            alpha=0.4
            image[idx[0], idx[1], :] *= 1.0 - alpha
            image[idx[0], idx[1], :] += alpha * color

        #for mask, color in zip(masks, colors):
        #    thresh = mask[0, :, :, None]
        #    contours, hierarchy = cv2_util.findContours(
        #        thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
        #    )
        #    image = cv2.drawContours(image, contours, -1, color, 3)

        composite = image

        return composite 

# 需要导入模块: from maskrcnn_benchmark.utils import cv2_util [as 别名]
# 或者: from maskrcnn_benchmark.utils.cv2_util import findContours [as 别名]
def instances2dict_with_polygons(imageFileList, verbose=False):
    imgCount     = 0
    instanceDict = {}

    if not isinstance(imageFileList, list):
        imageFileList = [imageFileList]

    if verbose:
        print("Processing {} images...".format(len(imageFileList)))

    for imageFileName in imageFileList:
        # Load image
        img = Image.open(imageFileName)

        # Image as numpy array
        imgNp = np.array(img)

        # Initialize label categories
        instances = {}
        for label in labels:
            instances[label.name] = []

        # Loop through all instance ids in instance image
        for instanceId in np.unique(imgNp):
            if instanceId < 1000:
                continue
            instanceObj = Instance(imgNp, instanceId)
            instanceObj_dict = instanceObj.toDict()

            #instances[id2label[instanceObj.labelID].name].append(instanceObj.toDict())
            if id2label[instanceObj.labelID].hasInstances:
                mask = (imgNp == instanceId).astype(np.uint8)
                contour, hier = cv2_util.findContours(
                    mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

                polygons = [c.reshape(-1).tolist() for c in contour]
                instanceObj_dict['contours'] = polygons

            instances[id2label[instanceObj.labelID].name].append(instanceObj_dict)

        imgKey = os.path.abspath(imageFileName)
        instanceDict[imgKey] = instances
        imgCount += 1

        if verbose:
            print("\rImages Processed: {}".format(imgCount), end=' ')
            sys.stdout.flush()

    if verbose:
        print("")

    return instanceDict