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From: Thomas Passin <list1@tompassin.net>
Newsgroups: comp.lang.python
Subject: Re: Predicting an object over an pretrained model is not working
Date: Tue, 30 Jul 2024 15:25:39 -0400
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Message-ID: <mailman.48.1722368082.2981.python-list@python.org>
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On 7/30/2024 2:18 PM, marc nicole via Python-list wrote:
> 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
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