Python maskrcnn_benchmark.utils.cv2_util.findContours() Examples

The following are 29 code examples of maskrcnn_benchmark.utils.cv2_util.findContours(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module maskrcnn_benchmark.utils.cv2_util , or try the search function .
Example #1
Source File: predictor.py    From remote_sensing_object_detection_2019 with MIT License 6 votes vote down vote up 
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
Example #2
Source File: predictor.py    From RRPN_pytorch with MIT License 6 votes vote down vote up 
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
Example #3
Source File: predictor.py    From training with Apache License 2.0 6 votes vote down vote up 
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
Example #4
Source File: predictor.py    From RRPN_pytorch with MIT License 6 votes vote down vote up 
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
Example #5
Source File: predictor.py    From maskrcnn-benchmark with MIT License 6 votes vote down vote up 
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
Example #6
Source File: segmentation_mask.py    From maskrcnn-benchmark with MIT License 6 votes vote down vote up 
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
Example #7
Source File: predictor.py    From RRPN_pytorch with MIT License 6 votes vote down vote up 
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
Example #8
Source File: segmentation_mask.py    From sampling-free with MIT License 6 votes vote down vote up 
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
Example #9
Source File: predictor.py    From argoverse_baselinetracker with MIT License 6 votes vote down vote up 
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
Example #10
Source File: predictor.py    From remote_sensing_object_detection_2019 with MIT License 6 votes vote down vote up 
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
Example #11
Source File: predictor.py    From remote_sensing_object_detection_2019 with MIT License 6 votes vote down vote up 
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
Example #12
Source File: predictor.py    From DF-Traffic-Sign-Identification with MIT License 6 votes vote down vote up 
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
Example #13
Source File: predictor.py    From DetNAS with MIT License 6 votes vote down vote up 
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
Example #14
Source File: segmentation_mask.py    From DetNAS with MIT License 6 votes vote down vote up 
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
Example #15
Source File: segmentation_mask.py    From Clothing-Detection with GNU General Public License v3.0 6 votes vote down vote up 
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
Example #16
Source File: predictor.py    From R2CNN.pytorch with MIT License 6 votes vote down vote up 
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
Example #17
Source File: predictor.py    From Res2Net-maskrcnn with MIT License 6 votes vote down vote up 
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
Example #18
Source File: predictor.py    From sampling-free with MIT License 5 votes vote down vote up 
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
Example #19
Source File: instances2dict_with_polygons.py    From TinyBenchmark with MIT License 4 votes vote down vote up 
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
Example #20
Source File: instances2dict_with_polygons.py    From RRPN_pytorch with MIT License 4 votes vote down vote up 
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
Example #21
Source File: instances2dict_with_polygons.py    From DF-Traffic-Sign-Identification with MIT License 4 votes vote down vote up 
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
Example #22
Source File: instances2dict_with_polygons.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 4 votes vote down vote up 
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
Example #23
Source File: instances2dict_with_polygons.py    From training with Apache License 2.0 4 votes vote down vote up 
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
Example #24
Source File: instances2dict_with_polygons.py    From maskrcnn-benchmark with MIT License 4 votes vote down vote up 
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
Example #25
Source File: instances2dict_with_polygons.py    From sampling-free with MIT License 4 votes vote down vote up 
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
Example #26
Source File: instances2dict_with_polygons.py    From remote_sensing_object_detection_2019 with MIT License 4 votes vote down vote up 
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
Example #27
Source File: instances2dict_with_polygons.py    From DetNAS with MIT License 4 votes vote down vote up 
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
Example #28
Source File: instances2dict_with_polygons.py    From R2CNN.pytorch with MIT License 4 votes vote down vote up 
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
Example #29
Source File: instances2dict_with_polygons.py    From Res2Net-maskrcnn with MIT License 4 votes vote down vote up 
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