for k, v in data_pca.iteritems(): data_pca[k] = (v + meanshape) - mean for k, v in data_patches.iteritems(): data_patches[k] = (v + meanshape) - mean # TODO: add values so that model is equally as wide/high as cropped images # in our case: 170 x 178, i.e. add 85 to x-vals, and 89 to y-vals meanshape = ((meanshape - mean) + [cropsize[0] / 2, cropsize[1] / 2]) if buildPatches: # weights for eyes and mouth from buildlib.me_weights import weights # build patch model patchModel = build_patches(data_patches, gradient=True, lbp=True, weights=weights, optimize_params=True) #patchModel = build_patches(data_patches) # store the model model = {} if buildPatches: model['patchModel'] = patchModel model['patchModel']['canvasSize'] = [cropsize[0], cropsize[1]] if buildScoring: model['scoring'] = scoring model['shapeModel'] = {} model['shapeModel']['eigenVectors'] = eigenVectors.T.tolist() model['shapeModel']['eigenValues'] = eigenValues model['shapeModel']['meanShape'] = meanshape.tolist()
for k,v in data_pca.iteritems(): data_pca[k] = (v+meanshape)-mean for k,v in data_patches.iteritems(): data_patches[k] = (v+meanshape)-mean # TODO: add values so that model is equally as wide/high as cropped images # in our case: 170 x 178, i.e. add 85 to x-vals, and 89 to y-vals meanshape = ((meanshape-mean)+[cropsize[0]/2,cropsize[1]/2]) if buildPatches: # weights for eyes and mouth from buildlib.me_weights import weights # build patch model patchModel = build_patches(data_patches, gradient=True, lbp=True, weights=weights, optimize_params=True) #patchModel = build_patches(data_patches) # store the model model = {} if buildPatches: model['patchModel'] = patchModel model['patchModel']['canvasSize'] = [cropsize[0],cropsize[1]] if buildScoring: model['scoring'] = scoring model['shapeModel'] = {} model['shapeModel']['eigenVectors'] = eigenVectors.T.tolist() model['shapeModel']['eigenValues'] = eigenValues model['shapeModel']['meanShape'] = meanshape.tolist() model['shapeModel']['numEvalues'] = len(eigenValues) model['shapeModel']['numPtsPerSample'] = meanshape.shape[0]
if buildScoring: scoring = getScoring(data_pca, meanshape-mean) for k,v in data_pca.iteritems(): data_pca[k] = (v+meanshape)-mean for k,v in data_patches.iteritems(): data_patches[k] = (v+meanshape)-mean # TODO: add values so that model is equally as wide/high as cropped images # in our case: 170 x 178, i.e. add 85 to x-vals, and 89 to y-vals meanshape = ((meanshape-mean)+[cropsize[0]/2,cropsize[1]/2]) if buildPatches: # build patch model patchModel = build_patches(data_patches, 0.00000001, False) # store the model model = {} if buildPatches: model['patchModel'] = patchModel model['patchModel']['canvasSize'] = [cropsize[0],cropsize[1]] if buildScoring: model['scoring'] = scoring model['shapeModel'] = {} model['shapeModel']['eigenVectors'] = eigenVectors.T.tolist() model['shapeModel']['eigenValues'] = eigenValues model['shapeModel']['meanShape'] = meanshape.tolist() model['shapeModel']['numEvalues'] = len(eigenValues) model['shapeModel']['numPtsPerSample'] = meanshape.shape[0] model['shapeModel']['nonRegularizedVectors'] = [0]