diff --git a/.gitignore b/.gitignore
index 558ed45..045e93a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,326 @@
+# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm,macos
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm,macos
+
+### macOS ###
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+# Thumbnails
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+### macOS Patch ###
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+### PyCharm ###
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+share/python-wheels/
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+.installed.cfg
+*.egg
+MANIFEST
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+# PyInstaller
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+# before PyInstaller builds the exe, so as to inject date/other infos into it.
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+# Installer logs
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+ipython_config.py
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+# pyenv
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+# having no cross-platform support, pipenv may install dependencies that don't work, or not
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+#pdm.lock
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+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
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+# SageMath parsed files
+*.sage.py
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+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
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+dmypy.json
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+# Pyre type checker
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+
+# pytype static type analyzer
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+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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+
+### Python Patch ###
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+
+
+# End of https://www.toptal.com/developers/gitignore/api/python,pycharm,macos
+
+
*.pyc
*.sh
-log_test/
-result_1024
-
+.idea/
+data/images_test/
+result_512/
+result_1024/
+results/
\ No newline at end of file
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/DPR.iml b/.idea/DPR.iml
new file mode 100644
index 0000000..7ab195b
--- /dev/null
+++ b/.idea/DPR.iml
@@ -0,0 +1,18 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
new file mode 100644
index 0000000..40ca8ce
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,35 @@
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..d8bd76f
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..0b7d245
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/DPR.py b/DPR.py
new file mode 100644
index 0000000..9ea443e
--- /dev/null
+++ b/DPR.py
@@ -0,0 +1,170 @@
+import os
+
+from alive_progress import alive_bar
+from torch.autograd import Variable
+import torch
+import cv2
+
+from configs.load_configs import configs
+from utils.utils_SH import *
+
+if configs["mode"] == "mode_512":
+ from model.defineHourglass_512_gray_skip import *
+else:
+ from model.defineHourglass_1024_gray_skip_matchFeature import *
+
+
+def video(predictor, n_lighting, fps=5, name=os.path.basename(configs['path_image']).split('.')[0]):
+ print(f'Processing image {name} ------ \nPlease Uong mieng nuoc & an mieng banh de...')
+ image = cv2.imread(configs["path_image"])
+ frame_width = image.shape[1] * 2
+ frame_height = image.shape[0]
+ size = (frame_width, frame_height)
+ os.makedirs('results/', exist_ok=True)
+ saved_path = f'results/{name}.mp4'
+ out = cv2.VideoWriter(saved_path,
+ cv2.VideoWriter_fourcc('m', 'p', '4', 'v'),
+ fps, size)
+
+ with alive_bar(total=n_lighting, theme='musical', length=200) as bar:
+ for i in range(n_lighting):
+ try:
+ frame = predictor(i)
+ out.write(frame)
+ bar()
+ except KeyboardInterrupt:
+ print("Stoped!")
+ out.release()
+ break
+ out.release()
+ print(f"Video saved in: {saved_path}")
+
+
+class DPR:
+ def __init__(self, img=configs["path_image"], device=None):
+ self.mode = configs["mode"]
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is not None else device
+ self.img = cv2.imread(img) if type(img) is str else img
+ self.img_ori = self.img.copy()
+ self.row, self.col = self.img.shape[:2]
+ self.img = cv2.resize(self.img, tuple((int(configs[self.mode]['size']), int(configs[self.mode]['size']))))
+ self.Lab = cv2.cvtColor(self.img, cv2.COLOR_BGR2LAB)
+ self.inputL = self._process_inputL()
+
+ # load model
+ if self.mode == 'mode_512':
+ self.my_network = HourglassNet()
+
+ elif self.mode == 'mode_1024':
+ my_network_512 = HourglassNet(16)
+ self.my_network = HourglassNet_1024(my_network_512, 16)
+
+ self.my_network.load_state_dict(
+ torch.load(os.path.join(configs["modelFolder"], configs[self.mode]["checkpoint"])))
+ self.my_network.to(self.device)
+ self.my_network.train(False)
+
+ def _process_inputL(self):
+ inputL = self.Lab[:, :, 0]
+ inputL = inputL.astype(np.float32) / 255.0
+ inputL = inputL.transpose((0, 1))
+ inputL = inputL[None, None, ...]
+ inputL = Variable(torch.from_numpy(inputL).to(self.device))
+ return inputL
+
+ def _process_output(self, outputImg):
+ outputImg = outputImg[0].cpu().data.numpy()
+ outputImg = outputImg.transpose((1, 2, 0))
+ outputImg = np.squeeze(outputImg)
+ outputImg = (outputImg * 255.0).astype(np.uint8)
+ return outputImg
+
+ def _create_sh(self, i):
+ # rendering half-sphere
+ sh = np.loadtxt(os.path.join(configs["lightFolder"], 'rotate_light_{:02d}.txt'.format(i)))
+ sh = sh[0:9]
+ sh = sh * 0.7
+ sh = np.squeeze(sh)
+ return sh
+
+ def _create_normal_and_valid(self, img_size=256):
+ # ---------------- create normal for rendering half sphere ------
+ img_size = 256
+ x = np.linspace(-1, 1, img_size)
+ z = np.linspace(1, -1, img_size)
+ x, z = np.meshgrid(x, z)
+
+ mag = np.sqrt(x ** 2 + z ** 2)
+ valid = mag <= 1
+ y = -np.sqrt(1 - (x * valid) ** 2 - (z * valid) ** 2)
+ x = x * valid
+ y = y * valid
+ z = z * valid
+ normal = np.concatenate((x[..., None], y[..., None], z[..., None]), axis=2)
+ normal = np.reshape(normal, (-1, 3))
+ return normal, valid
+
+ def _get_shading(self, normal, SH):
+ """
+ get shading based on normals and SH
+ normal is Nx3 matrix
+ SH: 9 x m vector
+ return Nxm vector, where m is the number of returned images
+ """
+ sh_basis = SH_basis(normal)
+ shading = np.matmul(sh_basis, SH)
+ return shading
+
+ def __call__(self, i):
+ # for i in range(n_lighting):
+ sh = self._create_sh(i)
+ normal, valid = self._create_normal_and_valid()
+ shading = self._get_shading(normal, sh)
+ value = np.percentile(shading, 10)
+ ind = shading > value
+ shading[ind] = value
+ shading = (shading - np.min(shading)) / (np.max(shading) - np.min(shading))
+ shading = (shading * 255.0).astype(np.uint8)
+ shading = np.reshape(shading, (256, 256))
+ shading = shading * valid
+
+ # Save visualize SH
+ if configs["saveVisualize"]:
+ cv2.imwrite(os.path.join(configs[self.mode]["saveFolder"], 'light_{:02d}.png'.format(i)), shading)
+
+ # rendering images using the network
+ sh = np.reshape(sh, (1, 9, 1, 1)).astype(np.float32)
+ sh = Variable(torch.from_numpy(sh).to(self.device))
+
+ if self.mode == "mode_1024":
+ outputImg, _, outputSH, _ = self.my_network(self.inputL, sh, 0)
+ else:
+ outputImg, outputSH = self.my_network(self.inputL, sh, 0)
+
+ outputImg = self._process_output(outputImg)
+ self.Lab[:, :, 0] = outputImg
+ resultLab = cv2.cvtColor(self.Lab, cv2.COLOR_LAB2BGR)
+ resultLab = cv2.resize(resultLab, (self.col, self.row))
+
+ final = cv2.hconcat([self.img_ori, resultLab])
+
+ if configs["showResult"]:
+ cv2.imshow("shading", shading)
+ cv2.imshow(configs[self.mode]["windowName"], final)
+ cv2.waitKey(0)
+
+ if configs["saveResult"]:
+ path_saveFolder = os.path.join(configs[self.mode]["saveFolder"],
+ os.path.basename(configs["path_image"])[:-4])
+ os.makedirs(path_saveFolder, exist_ok=True)
+
+ cv2.imwrite(os.path.join(path_saveFolder, str(i) + ".jpg"), resultLab)
+ cv2.imwrite(os.path.join(path_saveFolder, "hconcat_" + str(i) + ".jpg"), final)
+
+ return final
+
+
+if __name__ == "__main__":
+ dpr = DPR()
+ n_lighting = len(os.listdir(configs["lightFolder"]))
+ video(predictor=dpr, fps=5, n_lighting=n_lighting, name='face1_light3')
diff --git a/README.md b/README.md
index 9f8de56..9bed831 100644
--- a/README.md
+++ b/README.md
@@ -2,26 +2,27 @@
## Deep Single-Image Portrait Relighting [[Project Page]](http://zhhoper.github.io/dpr.html)
Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs. In ICCV, 2019
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+
### Overview
- - Test script for 512x512 images: `testNetwork_demo_512.py`
- - Test script for 1024x1024 images: `testNetwork_demo_1024.py`
+```bash
+python3 inference.py
+```
### Dependencies ###
pytorch >= 1.0.0