From 1e91e542a8744375bda1e8cf4c5998df334fc0c8 Mon Sep 17 00:00:00 2001 From: Somshubra Majumdar Date: Wed, 28 Jun 2017 15:08:34 -0500 Subject: [PATCH] Improved color transfer --- color_transfer.py | 52 ++++++++++++++++++++----- script_helper/NeuralStyleTransfer.ico | Bin 270398 -> 0 bytes script_helper/Script/color_transfer.py | 52 ++++++++++++++++++++----- 3 files changed, 86 insertions(+), 18 deletions(-) delete mode 100644 script_helper/NeuralStyleTransfer.ico diff --git a/color_transfer.py b/color_transfer.py index 55ed9b4..c42026e 100644 --- a/color_transfer.py +++ b/color_transfer.py @@ -10,13 +10,47 @@ # Util function to match histograms -def match_histograms(A, B, rng=(0.0, 255.0), bins=64): - (Ha, Xa), (Hb, Xb) = [np.histogram(i, bins=bins, range=rng, density=True) for i in [A, B]] - X = np.linspace(rng[0], rng[1], bins, endpoint=True) - Hpa, Hpb = [np.cumsum(i) * (rng[1] - rng[0]) ** 2 / float(bins) for i in [Ha, Hb]] - inv_Ha = interp1d(X, Hpa, bounds_error=False, fill_value='extrapolate') - map_Hb = interp1d(Hpb, X, bounds_error=False, fill_value='extrapolate') - return map_Hb(inv_Ha(A).clip(0.0, 255.0)) +def match_histograms(source, template): + """ + Adjust the pixel values of a grayscale image such that its histogram + matches that of a target image + + Arguments: + ----------- + source: np.ndarray + Image to transform; the histogram is computed over the flattened + array + template: np.ndarray + Template image; can have different dimensions to source + Returns: + ----------- + matched: np.ndarray + The transformed output image + """ + + oldshape = source.shape + source = source.ravel() + template = template.ravel() + + # get the set of unique pixel values and their corresponding indices and + # counts + s_values, bin_idx, s_counts = np.unique(source, return_inverse=True, + return_counts=True) + t_values, t_counts = np.unique(template, return_counts=True) + + # take the cumsum of the counts and normalize by the number of pixels to + # get the empirical cumulative distribution functions for the source and + # template images (maps pixel value --> quantile) + s_quantiles = np.cumsum(s_counts).astype(np.float64) + s_quantiles /= s_quantiles[-1] + t_quantiles = np.cumsum(t_counts).astype(np.float64) + t_quantiles /= t_quantiles[-1] + + # interpolate linearly to find the pixel values in the template image + # that correspond most closely to the quantiles in the source image + interp_t_values = np.interp(s_quantiles, t_quantiles, t_values) + + return interp_t_values[bin_idx].reshape(oldshape) # util function to preserve image color @@ -55,8 +89,8 @@ def load_mask(mask_path, shape): mask[mask <= 127] = 0 mask[mask > 128] = 255 - max = np.amax(mask) - mask /= max + mask /= 255 + mask = mask.astype(np.int32) return mask diff --git a/script_helper/NeuralStyleTransfer.ico b/script_helper/NeuralStyleTransfer.ico deleted file mode 100644 index 2664a59bae091c266754a944491c77977ad0ab4c..0000000000000000000000000000000000000000 GIT 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(Ha, Xa), (Hb, Xb) = [np.histogram(i, bins=bins, range=rng, density=True) for i in [A, B]] - X = np.linspace(rng[0], rng[1], bins, endpoint=True) - Hpa, Hpb = [np.cumsum(i) * (rng[1] - rng[0]) ** 2 / float(bins) for i in [Ha, Hb]] - inv_Ha = interp1d(X, Hpa, bounds_error=False, fill_value='extrapolate') - map_Hb = interp1d(Hpb, X, bounds_error=False, fill_value='extrapolate') - return map_Hb(inv_Ha(A).clip(0.0, 255.0)) +def match_histograms(source, template): + """ + Adjust the pixel values of a grayscale image such that its histogram + matches that of a target image + + Arguments: + ----------- + source: np.ndarray + Image to transform; the histogram is computed over the flattened + array + template: np.ndarray + Template image; can have different dimensions to source + Returns: + ----------- + matched: np.ndarray + The transformed output image + """ + + oldshape = source.shape + source = source.ravel() + template = template.ravel() + + # get the set of unique pixel values and their corresponding indices and + # counts + s_values, bin_idx, s_counts = np.unique(source, return_inverse=True, + return_counts=True) + t_values, t_counts = np.unique(template, return_counts=True) + + # take the cumsum of the counts and normalize by the number of pixels to + # get the empirical cumulative distribution functions for the source and + # template images (maps pixel value --> quantile) + s_quantiles = np.cumsum(s_counts).astype(np.float64) + s_quantiles /= s_quantiles[-1] + t_quantiles = np.cumsum(t_counts).astype(np.float64) + t_quantiles /= t_quantiles[-1] + + # interpolate linearly to find the pixel values in the template image + # that correspond most closely to the quantiles in the source image + interp_t_values = np.interp(s_quantiles, t_quantiles, t_values) + + return interp_t_values[bin_idx].reshape(oldshape) # util function to preserve image color @@ -55,8 +89,8 @@ def load_mask(mask_path, shape): mask[mask <= 127] = 0 mask[mask > 128] = 255 - max = np.amax(mask) - mask /= max + mask /= 255 + mask = mask.astype(np.int32) return mask