From e2ab2cc729bb3a5f927013b7336f42687c3b21d6 Mon Sep 17 00:00:00 2001 From: Koichi Ozaki Date: Fri, 6 Dec 2024 13:36:48 +0900 Subject: [PATCH] Implement detectree --- README.md | 1 + image_segmentation/detectree/LICENSE | 674 ++++++++++++++++++ image_segmentation/detectree/README.md | 61 ++ image_segmentation/detectree/detectree.py | 208 ++++++ .../detectree/detectree_utils/filters.py | 63 ++ .../detectree_utils/pixel_features.py | 158 ++++ image_segmentation/detectree/output.png | Bin 0 -> 10928 bytes image_segmentation/detectree/requirments.txt | 3 + image_segmentation/detectree/tile.png | Bin 0 -> 1263976 bytes scripts/download_all_models.sh | 1 + 10 files changed, 1169 insertions(+) create mode 100644 image_segmentation/detectree/LICENSE create mode 100644 image_segmentation/detectree/README.md create mode 100644 image_segmentation/detectree/detectree.py create mode 100644 image_segmentation/detectree/detectree_utils/filters.py create mode 100644 image_segmentation/detectree/detectree_utils/pixel_features.py create mode 100644 image_segmentation/detectree/output.png create mode 100644 image_segmentation/detectree/requirments.txt create mode 100644 image_segmentation/detectree/tile.png diff --git a/README.md b/README.md index fd967ec89..33bb97afd 100644 --- a/README.md +++ b/README.md @@ -455,6 +455,7 @@ If you would like to try on your computer: | [](image_segmentation/grounded_sam/) | [grounded_sam](/image_segmentation/grounded_sam/) | [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main) | Pytorch | 1.2.16 and later | | [](image_segmentation/segment-anything-2/) | [segment-anything-2](/image_segmentation/segment-anything-2/) | [Segment Anything 2](https://github.com/facebookresearch/segment-anything-2) | Pytorch | 1.2.16 and later | | [](image_segmentation/fast_sam/) | [fast_sam](/image_segmentation/fast_sam/) | [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) | Pytorch | 1.2.14 and later | +| [](image_segmentation/detectree/) | [detectree](/image_segmentation/detectree/) | [detectree](https://github.com/martibosch/detectree) | scikit-learn | | ## Large Language Model diff --git a/image_segmentation/detectree/LICENSE b/image_segmentation/detectree/LICENSE new file mode 100644 index 000000000..f288702d2 --- /dev/null +++ b/image_segmentation/detectree/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/image_segmentation/detectree/README.md b/image_segmentation/detectree/README.md new file mode 100644 index 000000000..9d74303a9 --- /dev/null +++ b/image_segmentation/detectree/README.md @@ -0,0 +1,61 @@ +# detectree + +## Input + + + +(Image from [https://wms.geo.admin.ch/?SERVICE=WMS&REQUEST=GetMap&VERSION=1.3.0&FORMAT=image/png&LAYERS=ch.swisstopo.images-swissimage&CRS=EPSG:2056&BBOX=2532980,1152150,2533380,1152450&WIDTH=800&HEIGHT=600](https://wms.geo.admin.ch/?SERVICE=WMS&REQUEST=GetMap&VERSION=1.3.0&FORMAT=image/png&LAYERS=ch.swisstopo.images-swissimage&CRS=EPSG:2056&BBOX=2532980,1152150,2533380,1152450&WIDTH=800&HEIGHT=600)) + +## Output + + + +## Requirements +This model requires additional module. + +``` +pip3 install -r requirments.txt +``` + +## Usage +Automatically downloads the onnx and prototxt files on the first run. +It is necessary to be connected to the Internet while downloading. + +For the sample image, +``` +$ python3 detectree.py +``` + +If you want to run in onnx mode, you specify `--onnx` option as below. +``` +$ python3 detectree.py --onnx +``` + +If you want to specify the input image, put the image path after the `--input` option. +You can use `--savepath` option to change the name of the output file to save. +``` +$ python3 detectree.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH +``` + +By adding the `--video` option, you can input the video. +If you pass `0` as an argument to VIDEO_PATH, you can use the webcam input instead of the video file. +``` +$ python3 detectree.py --video VIDEO_PATH +``` + +## Reference + +- [detectree](https://github.com/martibosch/detectree) + +## Framework + +PyTorch + +## Model Format + +ONNX opset=17 + +## Netron + +[detectree.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/detectree/detectree.onnx.prototxt) + diff --git a/image_segmentation/detectree/detectree.py b/image_segmentation/detectree/detectree.py new file mode 100644 index 000000000..124ff2196 --- /dev/null +++ b/image_segmentation/detectree/detectree.py @@ -0,0 +1,208 @@ +import os +import sys +import time + +import numpy as np +import cv2 +import rasterio as rio +import maxflow as mf + +import ailia + +from detectree_utils import pixel_features + +# import original modules +sys.path.append('../../util') +from arg_utils import get_base_parser, update_parser, get_savepath # noqa: E402 +from model_utils import check_and_download_models # noqa: E402 +from webcamera_utils import get_capture, get_writer # noqa: E402 + +# logger +from logging import getLogger # noqa: E402 +logger = getLogger(__name__) + +# ====================== +# Parameters 1 +# ====================== +IMAGE_PATH = 'tile.png' +SAVE_IMAGE_PATH = 'output.png' + +TREE_VAL = 255 +NONTREE_VAL = 0 +REFINE = True +REFINE_BETA = 50 +REFINE_INT_RESCALE = 10000 +MOORE_NEIGHBORHOOD_ARR = np.array([[0, 0, 0], [0, 0, 1], [1, 1, 1]]) + +# ====================== +# Arguemnt Parser Config +# ====================== +parser = get_base_parser( + 'detectree', IMAGE_PATH, SAVE_IMAGE_PATH +) +parser.add_argument( + '-o', '--onnx', action='store_true', + help="Option to use onnxrutime to run or not." +) +args = update_parser(parser) + +# ====================== +# Parameters 2 +# ====================== +WEIGHT_PATH = 'detectree.onnx' +MODEL_PATH = 'detectree.onnx.prototxt' +REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/detectree/' + +# ====================== +# Utils +# ====================== +def load_image(input_path): + with rio.open(input_path) as src: + arr = src.read() + return np.rollaxis(arr[:3], 0, 3) + +def preprocess(img): + return pixel_features.PixelFeaturesBuilder().build_features_from_arr(img) + +def post_process(output, img_shape): + if not REFINE: + y_pred = output[0].reshape(img_shape).astype(int)*TREE_VAL + else: + p_nontree, p_tree = np.hsplit(output[1], 2) + g = mf.Graph[int]() + node_ids = g.add_grid_nodes(img_shape) + P_nontree = p_nontree.reshape(img_shape) + P_tree = p_tree.reshape(img_shape) + + D_tree = (REFINE_INT_RESCALE * np.log(P_nontree)).astype(int) + D_nontree = (REFINE_INT_RESCALE * np.log(P_tree)).astype(int) + g.add_grid_edges(node_ids, REFINE_BETA, structure=MOORE_NEIGHBORHOOD_ARR) + g.add_grid_tedges(node_ids, D_tree, D_nontree) + g.maxflow() + y_pred = np.full(img_shape, NONTREE_VAL) + y_pred[g.get_grid_segments(node_ids)] = TREE_VAL + return y_pred + +def segment_image(img, net): + img_shape = img.shape[:2] + img = preprocess(img) + + if args.onnx: + input_name = net.get_inputs()[0].name + output = net.run(None, {input_name: img}) + else: + output = net.predict(img) + + out = post_process(output, img_shape) + out = out.astype(np.uint8) + return out + +# ====================== +# Main functions +# ====================== +def recognize_from_image(): + # net initialize + if args.onnx: + import onnxruntime + net = onnxruntime.InferenceSession(WEIGHT_PATH) + else: + logger.info(f'env_id: {args.env_id}') + net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id) + + # input image loop + for image_path in args.input: + # prepare input data + logger.info(image_path) + img = load_image(image_path) + logger.debug(f'input image shape: {img.shape}') + + # inference + logger.info('Start inference...') + if args.benchmark: + logger.info('BENCHMARK mode') + total_time_estimation = 0 + for i in range(args.benchmark_count): + start = int(round(time.time() * 1000)) + model_out = segment_image(img, net) + end = int(round(time.time() * 1000)) + estimation_time = (end - start) + + logger.info(f'\tailia processing estimation time {estimation_time} ms') + if i != 0: + total_time_estimation = total_time_estimation + estimation_time + + logger.info(f'\taverage time estimation {total_time_estimation / (args.benchmark_count - 1)} ms') + else: + model_out = segment_image(img, net) + + # save result + savepath = get_savepath(args.savepath, image_path) + logger.info(f'saved at : {savepath}') + cv2.imwrite(savepath, model_out) + logger.info('Script finished successfully.') + + +def recognize_from_video(): + # net initialize + if args.onnx: + import onnxruntime + net = onnxruntime.InferenceSession(WEIGHT_PATH) + else: + logger.info(f'env_id: {args.env_id}') + net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id) + + capture = get_capture(args.video) + video_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) + video_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + + if args.savepath != SAVE_IMAGE_PATH: + writer = get_writer(args.savepath, video_height, video_width) + else: + writer = None + + frame_names = None + frame_shown = False + frame_idx = 0 + while(True): + ret, img = capture.read() + + if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret: + break + if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0: + break + + model_out = segment_image(img, net) + print(type(model_out)) + print(model_out.dtype) + print(model_out.shape) + print(np.min(model_out), np.max(model_out)) + + cv2.imshow('frame', model_out) + if frame_names is not None: + cv2.imwrite(f'video_{frame_idx}.png', model_out) + + if writer is not None: + writer.write(model_out) + + frame_shown = True + frame_idx = frame_idx + 1 + + if writer is not None: + writer.release() + logger.info('Script finished successfully.') + + +def main(): + # model files check and download + check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH) + + if args.video is not None: + # video mode + recognize_from_video() + else: + # image mode + recognize_from_image() + + +if __name__ == '__main__': + main() diff --git a/image_segmentation/detectree/detectree_utils/filters.py b/image_segmentation/detectree/detectree_utils/filters.py new file mode 100644 index 000000000..ff3c01dd6 --- /dev/null +++ b/image_segmentation/detectree/detectree_utils/filters.py @@ -0,0 +1,63 @@ +"""Utilities to produce filters.""" + +import numpy as np + +def _gaussian_kernel1d(sigma, order, radius): + """ + Compute a 1-D Gaussian convolution kernel. + + From https://github.com/scipy/scipy/blob/v1.9.2/scipy/ndimage/_filters.py#L179-L207 + Copying it here since it is not part of scipy's public API. + See https://github.com/martibosch/detectree/issues/12 + """ + if order < 0: + raise ValueError("order must be non-negative") + exponent_range = np.arange(order + 1) + sigma2 = sigma * sigma + x = np.arange(-radius, radius + 1) + phi_x = np.exp(-0.5 / sigma2 * x**2) + phi_x = phi_x / phi_x.sum() + + if order == 0: + return phi_x + else: + q = np.zeros(order + 1) + q[0] = 1 + D = np.diag(exponent_range[1:], 1) # D @ q(x) = q'(x) + P = np.diag(np.ones(order) / -sigma2, -1) # P @ q(x) = q(x) * p'(x) + Q_deriv = D + P + for _ in range(order): + q = Q_deriv.dot(q) + q = (x[:, None] ** exponent_range).dot(q) + return q * phi_x + + +def _get_gaussian_kernel1d(sigma, *, order=0, truncate=4.0): + """Based on scipy.ndimage.filters.gaussian_filter1d.""" + sd = float(sigma) + # make the radius of the filter equal to truncate standard deviations + lw = int(truncate * sd + 0.5) + # # Since we are calling correlate, not convolve, revert the kernel + # weights = _gaussian_kernel1d(sigma, order, lw)[::-1] + weights = _gaussian_kernel1d(sigma, order, lw) + return weights + + +def get_texture_kernel(sigma): + """ + Get a texture kernel based on Yang et al. (2009). + + Parameters + ---------- + sigma : numeric + Scale parameter to build a texture kernel, based on a Gaussian on the + X dimension and a second-derivative Gaussian in the Y dimension + + Returns + ------- + texture_kernel : array-like + """ + g0_kernel_arr = _get_gaussian_kernel1d(sigma, order=0) + g2_kernel_arr = _get_gaussian_kernel1d(sigma, order=2) + + return np.dot(g2_kernel_arr.reshape(1, -1).T, g0_kernel_arr.reshape(1, -1)) diff --git a/image_segmentation/detectree/detectree_utils/pixel_features.py b/image_segmentation/detectree/detectree_utils/pixel_features.py new file mode 100644 index 000000000..043d2bb83 --- /dev/null +++ b/image_segmentation/detectree/detectree_utils/pixel_features.py @@ -0,0 +1,158 @@ +"""Build pixel features.""" + +import cv2 +import numpy as np +from scipy import ndimage as ndi +from skimage import color, morphology, transform +from skimage.filters import rank + +from . import filters + +# to convert to illumination-invariant color space +# https://www.cs.harvard.edu/~sjg/papers/cspace.pdf +B = np.array( + [ + [0.9465229, 0.2946927, -0.1313419], + [-0.1179179, 0.9929960, 0.007371554], + [0.09230461, -0.04645794, 0.9946464], + ] +) + +A = np.array( + [ + [27.07439, -22.80783, -1.806681], + [-5.646736, -7.722125, 12.86503], + [-4.163133, -4.579428, -4.576049], + ] +) + +NUM_RGB_CHANNELS = 3 +NUM_LAB_CHANNELS = 3 +NUM_XYZ_CHANNELS = 3 +NUM_ILL_CHANNELS = 3 + + +class PixelFeaturesBuilder: + """Customize how pixel features are computed.""" + + def __init__( + self, + *, + sigmas=[1, np.sqrt(2), 2], + num_orientations=6, + neighborhood=None, + min_neighborhood_range=2, + num_neighborhoods=3, + ): + """ + Initialize the pixel feature builder. + + See the `background `_ example notebook for more + details. + + Parameters + ---------- + sigmas : list-like, optional + The list of scale parameters (sigmas) to build the Gaussian filter bank that + will be used to compute the pixel-level features. The provided argument will + be passed to the initialization method of the `PixelFeaturesBuilder` + class. + num_orientations : int, optional + The number of equally-distributed orientations to build the Gaussian filter + bank that will be used to compute the pixel-level features. The provided + argument will be passed to the initialization method of the + `PixelFeaturesBuilder` class. + neighborhood : array-like, optional + The base neighborhood structure that will be used to compute the entropy + features. The provided argument will be passed to the initialization method + of the `PixelFeaturesBuilder` class. If no value is provided, a square with + a side size of `2 * min_neighborhood_range + 1` is used. + min_neighborhood_range : int, optional + The range (i.e., the square radius) of the smallest neigbhorhood window that + will be used to compute the entropy features. The provided argument will be + passed to the initialization method of the `PixelFeaturesBuilder` class. + num_neighborhoods : int, optional + The number of neigbhorhood windows (whose size follows a geometric + progression starting at `min_neighborhood_range`) that will be used to + compute the entropy features. The provided argument will be passed to the + initialization method of the `PixelFeaturesBuilder` class. + """ + if neighborhood is None: + neighborhood = morphology.square(2 * min_neighborhood_range + 1) + + self.sigmas = sigmas + self.num_orientations = num_orientations + self.neighborhood = neighborhood + self.scales = np.geomspace( + 1, 2 ** (num_neighborhoods - 1), num_neighborhoods + ).astype(int) + + self.num_color_features = NUM_LAB_CHANNELS + NUM_ILL_CHANNELS + self.num_texture_features = num_orientations * len(sigmas) + self.num_entropy_features = num_neighborhoods + self.num_pixel_features = ( + self.num_color_features + + self.num_texture_features + + self.num_entropy_features + ) + + def build_features_from_arr(self, img_rgb): + """ + Build feature array from an RGB image array. + + Parameters + ---------- + img_rgb : numpy ndarray + The image in RGB format, i.e., in a 3-D array + + Returns + ------- + responses : numpy ndarray + Array with the pixel responses. + """ + num_rows, num_cols, _ = img_rgb.shape + num_pixels = num_rows * num_cols + img_lab = color.rgb2lab(img_rgb) + img_lab_l = img_lab[:, :, 0] # ACHTUNG: this is a view + + X = np.zeros((num_pixels, self.num_pixel_features), dtype=np.float32) + + # color features + img_lab_vec = img_lab.reshape(num_rows * num_cols, NUM_LAB_CHANNELS) + img_xyz_vec = color.rgb2xyz(img_rgb).reshape( + num_rows * num_cols, NUM_XYZ_CHANNELS + ) + img_ill_vec = np.dot( + A, np.log(np.dot(B, img_xyz_vec.transpose()) + 1) + ).transpose() + X[:, :NUM_LAB_CHANNELS] = img_lab_vec + X[:, NUM_LAB_CHANNELS : NUM_LAB_CHANNELS + NUM_ILL_CHANNELS] = img_ill_vec + + # texture features + for i, sigma in enumerate(self.sigmas): + base_kernel_arr = filters.get_texture_kernel(sigma) + for j, orientation in enumerate(range(self.num_orientations)): + theta = orientation * 180 / self.num_orientations + oriented_kernel_arr = ndi.rotate(base_kernel_arr, theta) + img_filtered = cv2.filter2D( + img_lab_l, ddepth=-1, kernel=oriented_kernel_arr + ) + img_filtered_vec = img_filtered.flatten() + X[:, self.num_color_features + i * self.num_orientations + j] = ( + img_filtered_vec + ) + + # entropy features + entropy_start = self.num_color_features + self.num_texture_features + X[:, entropy_start] = rank.entropy( + img_lab_l.astype(np.uint16), self.neighborhood + ).flatten() + + for i, factor in enumerate(self.scales[1:], start=1): + img = transform.resize( + transform.downscale_local_mean(img_lab_l, (factor, factor)), + img_lab_l.shape, + ).astype(np.uint16) + X[:, entropy_start + i] = rank.entropy(img, self.neighborhood).flatten() + + return X diff --git a/image_segmentation/detectree/output.png b/image_segmentation/detectree/output.png new file mode 100644 index 0000000000000000000000000000000000000000..14b5d244bb289d2e0a3845e83cbcaef7199f7a3e GIT binary patch literal 10928 zcmX|ne^e7^zIO;g5|Tlc-!uSLBc#o=&XGa8EDwI1tBJT=drQ zu%-VLqKm)a+`P%WI(_HrpOh(^{?)d4Lq7izf;8hrf%8l$7dtPoFY#p1Wn#Z@zPp+$ 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