Medical Landmarks.ipynb

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   "source": "%matplotlib inline\nimport numpy as np\nfrom pathlib import Path\n\nimport menpo.io as mio\nfrom menpo.io.input.extensions import image_landmark_types\nfrom menpo.shape import PointCloud\n\nfrom menpofit.feature import normalize_std, normalize_norm\nfrom menpofit.clm import CLM, GradientDescentCLMFitter\n\nfrom menpowidgets import visualize_images\nfrom menpowidgets import visualize_shape_model\n\nDATASET_PATH = Path('/home/pts08/Downloads/dental/Challenge1')\nIMAGES_PATH = DATASET_PATH / 'RawImage/TrainingData'\nLANDMARKS_PATH = DATASET_PATH / 'GroundTruthData/Training'",
   "execution_count": null,
   "outputs": []
  },
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   "metadata": {
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   "source": "class MedicalTXTImporter(object):\n    \n    def __init__(self, filepath):\n        self.filepath = filepath\n    \n    def build(self, asset=None):\n        # Load the first 19 lines which are landmarks\n        with open(self.filepath) as f:\n            data = np.array([map(int, x.strip().split(',')) \n                             for x in f.readlines()[:19]])\n        # Flip the x,y coordinates\n        return PointCloud(data[:, ::-1])\n        \nimage_landmark_types['.txt'] = MedicalTXTImporter\n\ndef medical_landmark_resolver(filepath):\n    return {'txt': LANDMARKS_PATH / (filepath.stem + '.txt')}",
   "execution_count": null,
   "outputs": []
  },
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   "source": "def preprocess(image):\n    # Save some memory by halfing the image sizes\n    return image.rescale(0.5)\n\nimages = mio.import_images(IMAGES_PATH, \n                           landmark_resolver=medical_landmark_resolver)\nimages = list(images.map(preprocess))",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
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   "cell_type": "code",
   "source": "visualize_images(images)",
   "execution_count": null,
   "outputs": []
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   "source": "clm = CLM(images, \n          verbose=True,\n          scales=(0.25, 0.5, 1.0),\n          diagonal=250,\n          patch_normalisation=normalize_std,\n          patch_shape=(17, 17))",
   "execution_count": null,
   "outputs": []
  },
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   "metadata": {
    "trusted": true,
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   "cell_type": "code",
   "source": "fitter = GradientDescentCLMFitter(clm, n_shape=[3, 5, 15])",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
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   "cell_type": "code",
   "source": "im = images[0]\ngt_shape = im.landmarks['txt'].lms\nresult = fitter.fit_from_bb(im, gt_shape.bounding_box(), gt_shape=gt_shape, \n                            max_iters=30)",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
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   "cell_type": "code",
   "source": "result.view_widget()",
   "execution_count": null,
   "outputs": []
  }
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