From bba102ff8c955192a22dc6d6d54c76f4b82c8f25 Mon Sep 17 00:00:00 2001 From: mushcatshiro Date: Sat, 22 Nov 2025 23:34:10 +0800 Subject: [PATCH] adding blur detector --- .gitignore | 3 +- .rgignore | 1 + application/blur_detection_pipeline.py | 16 + lumos/commons/distribution.py | 162 ++ lumos/core.py | 83 + lumos/transform/blurring.py | 122 + lumos/visualize/histogram.py | 30 + poetry.lock | 3441 ++++++++++++++++++++++++ 8 files changed, 3857 insertions(+), 1 deletion(-) create mode 100644 .rgignore create mode 100644 application/blur_detection_pipeline.py create mode 100644 lumos/commons/distribution.py create mode 100644 lumos/core.py create mode 100644 lumos/transform/blurring.py create mode 100644 lumos/visualize/histogram.py create mode 100644 poetry.lock diff --git a/.gitignore b/.gitignore index eed53fe..4c7cf38 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .cache/ .venv/ -.vscode/ \ No newline at end of file +.vscode/ +__pycache__/ diff --git a/.rgignore b/.rgignore new file mode 100644 index 0000000..63f1fef --- /dev/null +++ b/.rgignore @@ -0,0 +1 @@ +*.lock diff --git a/application/blur_detection_pipeline.py b/application/blur_detection_pipeline.py new file mode 100644 index 0000000..f2e9d5a --- /dev/null +++ b/application/blur_detection_pipeline.py @@ -0,0 +1,16 @@ +from pathlib import Path +import os + +from lumos.core import run_simple_pipeline +from lumos.transform.blurring import basic_blur_detector +from lumos.commons.distribution import make_histogram +from lumos.visualize.histogram import show_histogram + +steps = [ + basic_blur_detector(1000, True), + make_histogram(), + show_histogram(), +] + +run_simple_pipeline(Path(os.environ["IMGPATH"]), steps) + diff --git a/lumos/commons/distribution.py b/lumos/commons/distribution.py new file mode 100644 index 0000000..c500879 --- /dev/null +++ b/lumos/commons/distribution.py @@ -0,0 +1,162 @@ +import numpy as np +from scipy.stats import chisquare +import matplotlib.pyplot as plt +import cv2 + +from lumos.commons.shape_cvt import to_uint8 +from lumos.visualize.histogram import show_histogram + + +def make_histogram(bins=256, excl_zero=False, show=False): + def process(img: np.ndarray): + arr = np.copy(img) + if len(arr.shape) == 4: + assert arr.shape[0] == 1 + arr = arr.squeeze() + # arr = arr.reshape((arr.shape[-1], -1)) + if np.issubdtype(arr.dtype, np.floating): + rbin = 1.0 + else: + rbin = 256 + W, H, C = arr.shape + arr = arr.transpose((2, 0, 1)).reshape((C, W * H)) + ret = np.zeros((arr.shape[0], 256)) + for i in range(arr.shape[0]): + hist, bin_edges = np.histogram(arr[i, :], bins=bins, range=(0, rbin)) + ret[i] = hist + if excl_zero: + ret[i][0] = 0 + if show: + show_histogram()(ret) + return (img, None) + + return process + + +def histogram_equalization(bins=256, show=False, copy:bool=False): + def process(img: np.ndarray): + if len(img.shape) == 4: + assert img.shape[0] == 1 + img = img.squeeze() + arr = np.copy(img) # needed, numpy 1.24 doesnt guarantee a copy in reshape + if np.issubdtype(arr.dtype, np.floating): + arr = to_uint8(arr) + # arr = arr.reshape((arr.shape[0], -1)) + + arr2 = np.zeros(arr.shape).astype(np.uint8) + for i in range(arr.shape[0]): + pixels_total = arr[i, :].size + hist, bin_edges = np.histogram(arr[i, :], bins=bins, range=(0, bins)) + cdf = hist.cumsum() + cdf_min = cdf[cdf > 0].min() + cdf_normalized = np.round((cdf - cdf_min) * 255 / (pixels_total - cdf_min)) + cdf_normalized = cdf_normalized.astype(np.uint8) + # basically cdf_normalized is the LUT, each elem in arr[i, :] + # i.e. cdf_normalized[50] maps elm value to the value of cdf lut + arr2[i, :] = cdf_normalized[arr[i, :]].astype(np.uint8) + if show: + fig, ax = plt.subplots(1, 2) + ax[0].imshow(img) + ax[1].imshow(arr2) + plt.show() + plt.close() + if copy: + return img, None + return arr2, None + + return process + + +def histogram_similarity(mode, hist1: np.ndarray, hist2: np.ndarray): + """ + only supports 1D array comparison + + Args: + ----- + mode: one of `iou`, `l1`, `chi`, `intersection` + hist1: observed, array to be compared + hist2: expected, referrence array + + iou: + l1: + chi: need to meet 4 assumptions, each sample must be independent, mutually + exclusive, each frequency at least 5, categorical data's count of each cat. + kurtosis: + skewness: + + """ + assert hist1.shape == hist2.shape + assert len(hist1.shape) == 1 + """ + TODO + - KL Divergance + """ + if mode == "iou": + stacked = np.vstack((hist1, hist2)) + intersection = np.min(stacked, axis=0) + union = np.max(stacked, axis=0) + iou = np.sum(intersection) / np.sum(union) + elif mode == "l1": + diff = np.sum(hist1 - hist2) + elif mode == "chi": + chi2, p = chisquare(hist1, hist2) + elif mode == "intersection": + stacked = np.vstack((hist1, hist2)) + sum_of_min = np.sum(np.min(stacked, axis=0)) + low_mag_hist = np.min(np.sum(hist1), np.sum(hist2)) + intersection_dist = 1 - (sum_of_min / low_mag_hist) + return + + +def build_individual_channel_cdf(img: np.ndarray): + if len(img.shape) == 4: + _, h, w, c = img.shape + else: + h, w, c = img.shape + full_cdf = [] + for i in range(c): + hist, _ = np.histogram(img[:, :, i], bins=256, range=(0, 256)) + pmf = hist / (h * w) + cdf = np.cumsum(pmf) + cdf /= cdf[-1] + full_cdf.append(cdf) + return full_cdf + + +def build_rgb_cdf(img: np.ndarray): + n, h, w, c = img.shape + pixels_list = img.reshape(-1, 3) # into (H*W, C) + unique_colors, counts = np.unique(pixels_list, axis=0, return_counts=True) + pmf = counts / (h * w) + cdf = np.cumsum(pmf) + cdf /= cdf[-1] + return unique_colors, cdf + + +def get_rgb_counts(img: np.ndarray, topk=0): + pixels_list = img.reshape(-1, 3) # into (H*W, C) + unique_colors, counts = np.unique(pixels_list, axis=0, return_counts=True) + unique_colors = unique_colors[::-1] + counts = counts[::-1] + if topk > 0: + return unique_colors[:topk], counts[:topk] + return unique_colors, counts + + +def gamma_correction(gamma=0.3, show=False, copy=False): + def process(img: np.ndarray): + inv_gamma = 1.0 / gamma + table = np.array( + [((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)] + ).astype(np.uint8) + arr2 = cv2.LUT(img, table) + if show: + fig, ax = plt.subplots(1, 2) + ax[0].imshow(img) + ax[1].imshow(arr2) + plt.show() + plt.close() + if copy: + return (img, None) + return (arr2, None) + return process diff --git a/lumos/core.py b/lumos/core.py new file mode 100644 index 0000000..79a07cd --- /dev/null +++ b/lumos/core.py @@ -0,0 +1,83 @@ +from typing import Callable, List, Any +from pathlib import Path +from functools import wraps +import numpy as np # Assuming images are NumPy arrays + + +""" +signature for all processing steps: it takes in np.ndarray and is expected to +return the np.ndarray (modified or not) together with the persistent path. +Also, it implicitly expects a metadata of the same persistent filename for any +visualization operations in `show.py` +""" +class BaseResult: + pass + +ImageProcessor = Callable[[np.ndarray], BaseResult] + +def run_pipeline(src_fpath: Path, steps: List[ImageProcessor]): + """ + two modes: + image can be a `Path` to zarr or a directory that has list of images. + + move src_fpath and `build_dataset_metadata` out + """ + +class SimplePipelineConfig: + Reuse: bool = False + + +simplePipelineConfig = SimplePipelineConfig() + + +def run_simple_pipeline( + src_fpath: Path, + steps: List[ImageProcessor], +): + """ + does a for loop (images) over steps, expect tens of images. designed for + quick validations. + """ + from .load import read_one_image + + # TODO technically read_one_image can be part of pipeline/step + if src_fpath.is_file(): + raise ValueError("expect a directory not a file") + for file in src_fpath.iterdir(): + _, data = read_one_image(file) + for i, step in enumerate(steps): + print(f"in step {i} running {step.__name__}; reuse: {simplePipelineConfig.Reuse}") + if simplePipelineConfig.Reuse: + _, fpath = step(data) + else: + data, fpath = step(data) + return 0 + + +def update_pipeline_config(reuse): + def process(img: np.ndarray): + simplePipelineConfig.Reuse = reuse + return (img, None) + + return process + + +def build_pipeline_steps(config_fpath: Path) -> List[ImageProcessor]: + """ + reads yaml to config steps that have additional args + """ + return + +def validate_input(target: BaseResult): + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + if isinstance(args[0], target): + return func(*args, **kwargs) + else: + raise TypeError( + f"Expected {target_type.__name__}, provided {type(inp).__name__}" + ) + return wrapper + return decorator + diff --git a/lumos/transform/blurring.py b/lumos/transform/blurring.py new file mode 100644 index 0000000..f753d2e --- /dev/null +++ b/lumos/transform/blurring.py @@ -0,0 +1,122 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy.ndimage import gaussian_laplace, gaussian_filter + + +def normalize_float_array_for_imshow(arr: np.ndarray) -> np.ndarray: + min_val = np.amin(arr) + max_val = np.amax(arr) + if max_val - min_val == 0: + scaled_image = np.zeros_like(arr) + else: + scaled_image = (arr - min_val) / (max_val - min_val) + return scaled_image + + +def basic_blur_detector_bugged(bins, show): + def process(img: np.ndarray) -> np.ndarray: + """ + kept to further learn about uninitiated memory. + """ + _img = np.copy(img) + _img = np.squeeze(img) + _img = _img.astype(np.float64) + _img /= 255.0 + blurred = np.copy(_img) + blurred = gaussian_filter(blurred, 2) + gl3_img = gaussian_laplace(_img, sigma=1) + gl3_blurred = gaussian_laplace(blurred, sigma=1) + gl3_img_flat = gl3_img.ravel() + gl3_blurred_flat = gl3_blurred.ravel() + # it is quite effective to just quantify by np.var + # print(np.var(gl3_blurred)) + # print(np.var(gl3_img)) + _min = min(gl3_img_flat.min(), gl3_blurred_flat.min()) + _max = max(gl3_img_flat.max(), gl3_blurred_flat.max()) + bins_edges = np.linspace(_min, _max, bins) + hist_img, _ = np.histogram(gl3_img, bins_edges) + hist_img_counts = np.zeros_like(hist_img, dtype=np.float64) + np.log(hist_img, out=hist_img_counts, where=(hist_img > 0)) + hist_blurred, _ = np.histogram(gl3_blurred, bins_edges) + hist_blurred_counts = np.zeros_like(hist_blurred, dtype=np.float64) + np.log(hist_blurred, out=hist_blurred_counts, where=(hist_blurred > 0)) + + # hist_img = np.log(hist_img, where=(hist_img > 0)) + # hist_blurred = np.log(hist_blurred, where=(hist_blurred > 0)) + # bin_centers = np.linspace(_min, _max, bins) + # ax[0][2].bar(bin_centers, hist_img, width=_range/bins) + # ax[1][2].bar(bin_centers, hist_blurred, width=_range/bins) + + if show: + fig, ax = plt.subplots(2, 3) + ax[0][0].imshow(_img) + ax[1][0].imshow(blurred) + ax[0][1].imshow(normalize_float_array_for_imshow(gl3_img)) + ax[1][1].imshow(normalize_float_array_for_imshow(gl3_blurred)) + + ax[0][2].stairs(hist_img_counts, bins_edges, fill=True) + # ax[0][2].set_yscale('log') # <--- The Magic Line + ax[0][2].set_title("Edge Strength Distribution (Log)") + ax[1][2].stairs(hist_blurred_counts, bins_edges, fill=True) + # ax[1][2].set_yscale('log') # <--- The Magic Line + ax[1][2].set_title("Blurred Edge Strength (Log)") + plt.show() + plt.close() + return (blurred, Path) + + return process + + +def basic_blur_detector(bins: int, show: bool): + def process(img: np.ndarray) -> np.ndarray: + """ + original and blurred + calculate metric (gaussian_laplace (viz) -> peak_widths(print) + -> full width half max (print) + convolve2d is not a good blur as it creates high frequency artifacts (sharp + rings at edge, to viz and confirm?) which doesnt work well with laplacian + filter + TODO + how to deal with 2d array ops? how to keep nchw as long as possible? + """ + _img = np.copy(img) + _img = np.squeeze(img) + _img = _img.astype(np.float64) + _img /= 255.0 + blurred = np.copy(_img) + blurred = gaussian_filter(blurred, 2) + + gl3_img = gaussian_laplace(_img, sigma=1) + gl3_blurred = gaussian_laplace(blurred, sigma=1) + gl3_img_flat = gl3_img.ravel() + gl3_blurred_flat = gl3_blurred.ravel() + + _min = min(gl3_img_flat.min(), gl3_blurred_flat.min()) + _max = max(gl3_img_flat.max(), gl3_blurred_flat.max()) + bins_edges = np.linspace(_min, _max, bins) + + hist_img, _ = np.histogram(gl3_img, bins_edges) + hist_img_counts = np.zeros_like(hist_img, dtype=np.float64) + np.log(hist_img, out=hist_img_counts, where=(hist_img > 0)) + hist_blurred, _ = np.histogram(gl3_blurred, bins_edges) + hist_blurred_counts = np.zeros_like(hist_blurred, dtype=np.float64) + np.log(hist_blurred, out=hist_blurred_counts, where=(hist_blurred > 0)) + + if show: + fig, ax = plt.subplots(2, 3) + ax[0][0].imshow(_img) + ax[1][0].imshow(blurred) + ax[0][1].imshow(normalize_float_array_for_imshow(gl3_img)) + ax[1][1].imshow(normalize_float_array_for_imshow(gl3_blurred)) + + ax[0][2].stairs(hist_img_counts, bins_edges, fill=True) + # ax[0][2].set_yscale('log') # <--- The Magic Line + ax[0][2].set_title("Edge Strength Distribution (Log)") + ax[1][2].stairs(hist_blurred_counts, bins_edges, fill=True) + # ax[1][2].set_yscale('log') # <--- The Magic Line + ax[1][2].set_title("Blurred Edge Strength (Log)") + plt.show() + plt.close() + return (blurred, None) + + return process diff --git a/lumos/visualize/histogram.py b/lumos/visualize/histogram.py new file mode 100644 index 0000000..e068af2 --- /dev/null +++ b/lumos/visualize/histogram.py @@ -0,0 +1,30 @@ +from lumos.core import ImageProcessor + +import numpy as np +import matplotlib.pyplot as plt + + +def show_histogram() -> ImageProcessor: + """ + expects a numpy array of shape (N, bins) where N signifies how many separated + histogram to plot i.e. `plt.subplots(1, N)`. + + TODO + consider merge with `basic_blur_detector`/`plt.stairs` + """ + + def process(arr: np.ndarray): + N, bins = arr.shape + fig, ax = plt.subplots(1, N) + if N == 1: + ax.bar(arr[N]) + else: + for i in range(N): + ax[i].bar(np.arange(0, bins), arr[i, :]) + plt.show() + return arr, None + + return 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