Article <mailman.47.1722364504.2981.python-list@python.org>

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<mailman.47.1722364504.2981.python-list@python.org>

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From: marc nicole <mk1853387@gmail.com>
Newsgroups: comp.lang.python
Subject: Predicting an object over an pretrained model is not working
Date: Tue, 30 Jul 2024 20:18:42 +0200
Lines: 230
Message-ID: <mailman.47.1722364504.2981.python-list@python.org>
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Hello all,

I want to predict an object by given as input an image and want to have my
model be able to predict the label. I have trained a model using tensorflow
based on annotated database where the target object to predict was added to
the pretrained model. the code I am using is the following where I set the
target object image as input and want to have the prediction output:








class MultiObjectDetection():

    def __init__(self, classes_name):

        self._classes_name = classes_name
        self._num_classes = len(classes_name)

        self._common_params = {'image_size': 448, 'num_classes':
self._num_classes,
                'batch_size':1}
        self._net_params = {'cell_size': 7, 'boxes_per_cell':2,
'weight_decay': 0.0005}
        self._net = YoloTinyNet(self._common_params, self._net_params,
test=True)

    def predict_object(self, image):
        predicts = self._net.inference(image)
        return predicts

    def process_predicts(self, resized_img, predicts, thresh=0.2):
        """
        process the predicts of object detection with one image input.

        Args:
            resized_img: resized source image.
            predicts: output of the model.
            thresh: thresh of bounding box confidence.
        Return:
            predicts_dict: {"stick": [[x1, y1, x2, y2, scores1], [...]]}.
        """
        cls_num = self._num_classes
        bbx_per_cell = self._net_params["boxes_per_cell"]
        cell_size = self._net_params["cell_size"]
        img_size = self._common_params["image_size"]
        p_classes = predicts[0, :, :, 0:cls_num]
        C = predicts[0, :, :, cls_num:cls_num+bbx_per_cell] # two
bounding boxes in one cell.
        coordinate = predicts[0, :, :, cls_num+bbx_per_cell:] # all
bounding boxes position.

        p_classes = np.reshape(p_classes, (cell_size, cell_size, 1, cls_num))
        C = np.reshape(C, (cell_size, cell_size, bbx_per_cell, 1))

        P = C * p_classes # confidencefor all classes of all bounding
boxes (cell_size, cell_size, bounding_box_num, class_num) = (7, 7, 2,
1).

        predicts_dict = {}
        for i in range(cell_size):
            for j in range(cell_size):
                temp_data = np.zeros_like(P, np.float32)
                temp_data[i, j, :, :] = P[i, j, :, :]
                position = np.argmax(temp_data) # refer to the class
num (with maximum confidence) for every bounding box.
                index = np.unravel_index(position, P.shape)

                if P[index] > thresh:
                    class_num = index[-1]
                    coordinate = np.reshape(coordinate, (cell_size,
cell_size, bbx_per_cell, 4)) # (cell_size, cell_size,
bbox_num_per_cell, coordinate)[xmin, ymin, xmax, ymax]
                    max_coordinate = coordinate[index[0], index[1], index[2], :]

                    xcenter = max_coordinate[0]
                    ycenter = max_coordinate[1]
                    w = max_coordinate[2]
                    h = max_coordinate[3]

                    xcenter = (index[1] + xcenter) * (1.0*img_size /cell_size)
                    ycenter = (index[0] + ycenter) * (1.0*img_size /cell_size)

                    w = w * img_size
                    h = h * img_size
                    xmin = 0 if (xcenter - w/2.0 < 0) else (xcenter - w/2.0)
                    ymin = 0 if (xcenter - w/2.0 < 0) else (ycenter - h/2.0)
                    xmax = resized_img.shape[0] if (xmin + w) >
resized_img.shape[0] else (xmin + w)
                    ymax = resized_img.shape[1] if (ymin + h) >
resized_img.shape[1] else (ymin + h)

                    class_name = self._classes_name[class_num]
                    predicts_dict.setdefault(class_name, [])
                    predicts_dict[class_name].append([int(xmin),
int(ymin), int(xmax), int(ymax), P[index]])

        return predicts_dict

    def non_max_suppress(self, predicts_dict, threshold=0.5):
        """
        implement non-maximum supression on predict bounding boxes.
        Args:
            predicts_dict: {"stick": [[x1, y1, x2, y2, scores1], [...]]}.
            threshhold: iou threshold
        Return:
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