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@ -247,6 +247,18 @@ class Parser(object): |
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""" |
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return {channel: n for n, channel in enumerate(self.metadata["channels"])} |
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def _remove_unwanted_bytes(self, image_group_data, image_data_start, height, width): |
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# Remove unwanted 0-bytes that can appear in stitched images |
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number_of_true_channels = int(len(image_group_data[4:]) / (height * width)) |
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unwanted_bytes_len = (len(image_group_data[image_data_start:]))%(height*width) |
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if unwanted_bytes_len: |
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warnings.warn('Identified unwanted bytes in the ND2 file, possibly stitched.') |
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byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-unwanted_bytes_len+1, height*number_of_true_channels) |
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if all([0 == image_group_data[byte_ids[i]+i] for i in range(len(byte_ids))]): |
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warnings.warn('All unwanted bytes are zero-bytes, correctly removed.') |
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for i in range(len(byte_ids)): |
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del image_group_data[byte_ids[i]] |
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def _get_raw_image_data(self, image_group_number, channel_offset, height, width): |
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"""Reads the raw bytes and the timestamp of an image. |
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@ -273,17 +285,7 @@ class Parser(object): |
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# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design |
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# a data structure that way, please send the author of this library a message. |
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number_of_true_channels = int(len(image_group_data[4:]) / (height * width)) |
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# Remove unwanted 0-bytes that can appear in stitched images |
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unwanted_bytes_len = (len(image_group_data[image_data_start:]))%(height*width) |
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if unwanted_bytes_len: |
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warnings.warn('Identified unwanted bytes in the ND2 file, possibly stitched.') |
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byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-unwanted_bytes_len+1, height*number_of_true_channels) |
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if all([0 == image_group_data[byte_ids[i]+i] for i in range(len(byte_ids))]): |
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warnings.warn('All unwanted bytes are zero-bytes, correctly removed.') |
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for i in range(len(byte_ids)): |
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del image_group_data[byte_ids[i]] |
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self._remove_unwanted_bytes(image_group_data, image_data_start, height, width) |
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try: |
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width)) |
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except ValueError: |
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