Updated the ObjectDetectionAPI to run batches.

[gbrunner]

@lingtangraster @Rob-Fletcher @dwilson1988 I was finding that the TensorFlow/ObjectDetectionAPI.py wasn't running batches when I I defined a _batchsize. I implemented that in the Python class and it appears to work and run really fast, about a minute to detect ~900 objects in Pro using a _batchsize = 100:

Let me know if this is helpful or completely off base.

[lingtangraster]

Hi @gbrunner, thanks for your suggestions.

Batch support was done through vectorize method in TemplateBaseDetector class.

def vectorize(self, **pixelBlocks):
        input_image = pixelBlocks['raster_pixels']
        _, height, width = input_image.shape
        batch, batch_height, batch_width = \
            prf_utils.tile_to_batch(input_image,
                                    self.json_info['ImageHeight'],
                                    self.json_info['ImageWidth'],
                                    self.padding,
                                    fixed_tile_size=False)

        batch_bounding_boxes, batch_scores, batch_classes = self.inference(batch, **self.scalars)
        return prf_utils.batch_detection_results_to_tile_results(
            batch_bounding_boxes,
            batch_scores,
            batch_classes,
            self.json_info['ImageHeight'],
            self.json_info['ImageWidth'],
            self.padding,
            batch_width
        )

ObjectDetectionAPI's ChildObjectDetector class inherited it, the input of the inference method is already a batch image array, which is a 4-d numpy array with shape [batch, image_height, image_width, bands].

Therefore, as I can see there is no need for any code modification at present. Thanks again for your time in reporting this issue, please let me know if you have any other suggestions.