Repository: ibaiGorordo/HITNET-Stereo-Depth-estimation Branch: main Commit: a64564954bfa Files: 11 Total size: 13.0 KB Directory structure: gitextract_i5i3wbqa/ ├── .gitignore ├── LICENSE ├── README.md ├── drivingStereoTest.py ├── hitnet/ │ ├── __init__.py │ ├── hitnet.py │ └── utils_hitnet.py ├── imageDepthEstimation.py ├── models/ │ └── .gitkeep ├── requirements.txt └── videoDepthEstimation.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2021 Ibai Gorordo Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # HITNET-Stereo-Depth-estimation Python scripts for performing stereo depth estimation using the [HITNET Tensorflow model from Google Research](https://github.com/google-research/google-research/tree/master/hitnet). ![Hitnet stereo depth estimation](https://github.com/ibaiGorordo/HITNET-Stereo-Depth-estimation/blob/main/doc/img/out.jpg) *Stereo depth estimation on the cones images from the Middlebury dataset (https://vision.middlebury.edu/stereo/data/scenes2003/)* # Requirements * **OpenCV**, **numpy** and **tensorflo**. **pafy** (`pip install git+https://github.com/zizo-pro/pafy@b8976f22c19e4ab5515cacbfae0a3970370c102b`) and **youtube-dl** are required for youtube video inference. * For the drivingStereo dataset, download the data from: https://drivingstereo-dataset.github.io/ # Tensorflow models Download the tensorflow models from the [original repository](https://github.com/google-research/google-research/tree/master/hitnet) and save them into the **[models](https://github.com/ibaiGorordo/HITNET-Stereo-Depth-estimation/tree/main/models)** folder. # Examples * **Image inference**: ``` python imageDepthEstimation.py ``` * **Video inference**: ``` python videoDepthEstimation.py ``` * **DrivingStereo dataset inference**: ``` python drivingStereoTest.py ``` # [Inference video Example](https://youtu.be/ge2iN8Ga4Dg) ![!Hitnet stereo depth estimation on video](https://github.com/ibaiGorordo/HITNET-Stereo-Depth-estimation/blob/main/doc/img/hitnetDepthEstimation.gif) # References: * Hitnet model: https://github.com/google-research/google-research/tree/master/hitnet * DrivingStereo dataset: https://drivingstereo-dataset.github.io/ * Original paper: https://arxiv.org/abs/2007.12140 ================================================ FILE: drivingStereoTest.py ================================================ import cv2 import pafy import tensorflow as tf import numpy as np import glob from hitnet import HitNet, ModelType, draw_disparity, draw_depth, CameraConfig out = cv2.VideoWriter('outpy2.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30, (881*3,400)) # Get image list left_images = glob.glob('DrivingStereo images/left/*.jpg') left_images.sort() right_images = glob.glob('DrivingStereo images/right/*.jpg') right_images.sort() depth_images = glob.glob('DrivingStereo images/depth/*.png') depth_images.sort() # Select model type model_type = ModelType.middlebury # model_type = ModelType.flyingthings # model_type = ModelType.eth3d if model_type == ModelType.middlebury: model_path = "models/middlebury_d400.pb" elif model_type == ModelType.flyingthings: model_path = "models/flyingthings_finalpass_xl.pb" elif model_type == ModelType.eth3d: model_path = "models/eth3d.pb" camera_config = CameraConfig(0.546, 1000) max_distance = 50 # Initialize model hitnet_depth = HitNet(model_path, model_type, camera_config) cv2.namedWindow("Estimated depth", cv2.WINDOW_NORMAL) for left_path, right_path, depth_path in zip(left_images[1500:1700:2], right_images[1500:1700:2], depth_images[1500:1700:2]): # Read frame from the video left_img = cv2.imread(left_path) right_img = cv2.imread(right_path) depth_img = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED).astype(np.float32)/256 # Estimate the depth disparity_map = hitnet_depth(left_img, right_img) depth_map = hitnet_depth.get_depth() color_disparity = draw_disparity(disparity_map) color_depth = draw_depth(depth_map, max_distance) color_real_depth = draw_depth(depth_img, max_distance) cobined_image = np.hstack((left_img,color_real_depth, color_depth)) out.write(cobined_image) cv2.imshow("Estimated depth", cobined_image) # Press key q to stop if cv2.waitKey(1) == ord('q'): break out.release() cv2.destroyAllWindows() ================================================ FILE: hitnet/__init__.py ================================================ from hitnet.hitnet import HitNet from hitnet.utils_hitnet import * ================================================ FILE: hitnet/hitnet.py ================================================ import tensorflow as tf import numpy as np import time import cv2 from hitnet.utils_hitnet import * drivingStereo_config = CameraConfig(0.546, 1000) class HitNet(): def __init__(self, model_path, model_type=ModelType.eth3d, camera_config=drivingStereo_config): self.fps = 0 self.timeLastPrediction = time.time() self.frameCounter = 0 self.camera_config = camera_config # Initialize model self.model = self.initialize_model(model_path, model_type) def __call__(self, left_img, right_img): return self.estimate_disparity(left_img, right_img) def initialize_model(self, model_path, model_type): self.model_type = model_type with tf.io.gfile.GFile(model_path, "rb") as f: graph_def = tf.compat.v1.GraphDef() loaded = graph_def.ParseFromString(f.read()) # Wrap frozen graph to ConcreteFunctions if self.model_type == ModelType.flyingthings: model = wrap_frozen_graph(graph_def=graph_def, inputs="input:0", outputs=["reference_output_disparity:0","secondary_output_disparity:0"]) else: model = wrap_frozen_graph(graph_def=graph_def, inputs="input:0", outputs="reference_output_disparity:0") return model def estimate_disparity(self, left_img, right_img): input_tensor = self.prepare_input(left_img, right_img) # Perform inference on the image if self.model_type == ModelType.flyingthings: left_disparity, right_disparity = self.inference(input_tensor) self.disparity_map = left_disparity else: self.disparity_map = self.inference(input_tensor) return self.disparity_map def get_depth(self): return self.camera_config.f*self.camera_config.baseline/self.disparity_map def prepare_input(self, left_img, right_img): if (self.model_type == ModelType.eth3d): # Shape (1, None, None, 2) left_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY) right_img = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY) left_img = np.expand_dims(left_img,2) right_img = np.expand_dims(right_img,2) combined_img = np.concatenate((left_img, right_img), axis=-1) / 255.0 else: # Shape (1, None, None, 6) left_img = cv2.cvtColor(left_img, cv2.COLOR_BGR2RGB) right_img = cv2.cvtColor(right_img, cv2.COLOR_BGR2RGB) combined_img = np.concatenate((left_img, right_img), axis=-1) / 255.0 return tf.convert_to_tensor(np.expand_dims(combined_img, 0), dtype=tf.float32) def inference(self, input_tensor): output = self.model(input_tensor) return np.squeeze(output) ================================================ FILE: hitnet/utils_hitnet.py ================================================ from enum import Enum import tensorflow as tf import numpy as np import cv2 import urllib from dataclasses import dataclass class ModelType(Enum): eth3d = 0 middlebury = 1 flyingthings = 2 @dataclass class CameraConfig: baseline: float f: float def wrap_frozen_graph(graph_def, inputs, outputs): def _imports_graph_def(): tf.compat.v1.import_graph_def(graph_def, name="") wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []) import_graph = wrapped_import.graph return wrapped_import.prune( tf.nest.map_structure(import_graph.as_graph_element, inputs), tf.nest.map_structure(import_graph.as_graph_element, outputs)) def draw_disparity(disparity_map): disparity_map = disparity_map.astype(np.uint8) norm_disparity_map = (255*((disparity_map-np.min(disparity_map))/(np.max(disparity_map) - np.min(disparity_map)))) return cv2.applyColorMap(cv2.convertScaleAbs(norm_disparity_map,1), cv2.COLORMAP_MAGMA) def draw_depth(depth_map, max_dist): norm_depth_map = 255*(1-depth_map/max_dist) norm_depth_map[norm_depth_map < 0] =0 norm_depth_map[depth_map == 0] =0 return cv2.applyColorMap(cv2.convertScaleAbs(norm_depth_map,1), cv2.COLORMAP_MAGMA) def load_img(url): req = urllib.request.urlopen(url) arr = np.asarray(bytearray(req.read()), dtype=np.uint8) return cv2.imdecode(arr, -1) # 'Load it as it is' ================================================ FILE: imageDepthEstimation.py ================================================ import cv2 import tensorflow as tf import numpy as np from hitnet import HitNet, ModelType, draw_disparity, draw_depth, CameraConfig, load_img # Select model type # model_type = ModelType.middlebury # model_type = ModelType.flyingthings model_type = ModelType.eth3d if model_type == ModelType.middlebury: model_path = "models/middlebury_d400.pb" elif model_type == ModelType.flyingthings: model_path = "models/flyingthings_finalpass_xl.pb" elif model_type == ModelType.eth3d: model_path = "models/eth3d.pb" # Initialize model hitnet_depth = HitNet(model_path, model_type) # Load images left_img = load_img("https://vision.middlebury.edu/stereo/data/scenes2003/newdata/cones/im2.png") right_img = load_img("https://vision.middlebury.edu/stereo/data/scenes2003/newdata/cones/im6.png") # Estimate the depth disparity_map = hitnet_depth(left_img, right_img) color_disparity = draw_disparity(disparity_map) cobined_image = np.hstack((left_img, right_img, color_disparity)) cv2.namedWindow("Estimated disparity", cv2.WINDOW_NORMAL) cv2.imshow("Estimated disparity", cobined_image) cv2.waitKey(0) cv2.imwrite("out.jpg", cobined_image) cv2.destroyAllWindows() ================================================ FILE: models/.gitkeep ================================================ ================================================ FILE: requirements.txt ================================================ tensorflow>=2.6 opencv-python numpy imread-from-url ================================================ FILE: videoDepthEstimation.py ================================================ import cv2 import pafy import tensorflow as tf import numpy as np from hitnet import HitNet, ModelType, draw_disparity, draw_depth, CameraConfig # Initialize video # cap = cv2.VideoCapture("video.mp4") videoUrl = 'https://youtu.be/Yui48w71SG0' videoPafy = pafy.new(videoUrl) print(videoPafy.streams) cap = cv2.VideoCapture(videoPafy.getbestvideo().url) # Select model type # model_type = ModelType.middlebury # model_type = ModelType.flyingthings model_type = ModelType.eth3d if model_type == ModelType.middlebury: model_path = "models/middlebury_d400.pb" elif model_type == ModelType.flyingthings: model_path = "models/flyingthings_finalpass_xl.pb" elif model_type == ModelType.eth3d: model_path = "models/eth3d.pb" # Store baseline (m) and focal length (pixel) camera_config = CameraConfig(0.1, 320) max_distance = 5 # Initialize model hitnet_depth = HitNet(model_path, model_type, camera_config) cv2.namedWindow("Estimated depth", cv2.WINDOW_NORMAL) while cap.isOpened(): try: # Read frame from the video ret, frame = cap.read() if not ret: break except: continue # Extract the left and right images left_img = frame[:,:frame.shape[1]//3] right_img = frame[:,frame.shape[1]//3:frame.shape[1]*2//3] color_real_depth = frame[:,frame.shape[1]*2//3:] # Estimate the depth disparity_map = hitnet_depth(left_img, right_img) depth_map = hitnet_depth.get_depth() color_disparity = draw_disparity(disparity_map) color_depth = draw_depth(depth_map, max_distance) cobined_image = np.hstack((left_img,color_real_depth, color_depth)) cv2.imshow("Estimated depth", cobined_image) # Press key q to stop if cv2.waitKey(1) == ord('q'): break cap.release() cv2.destroyAllWindows()