trimmed unrelated codes and added README.md

README.md

@@ -0,0 +1,58 @@
+# Deep 3d Portrait from a Single Image
+
+## Summary
+
+This repostiory is forked from [sicxu/Deep3dPortrait](https://github.com/sicxu/Deep3dPortrait).
+
+## Prepare GAN Training
+
+```
+git clone https://github.com/lylajeon/Deep3dPortrait Deep3dPortrait
+cd Deep3dPortrait
+git clone https://github.com/kingsj0405/Face-Landmark-Parsing face-parsing.PyTorch
+cd ..
+```
+Put all files in outputs/step4 to step5_ui_expression&pose_change/result
+
+```
+python integrated_process.py --input_img [IMG_NAME]
+cd step5_ui_expression&pose_change
+chmod +x run.sh
+./run.sh
+```
+
+# Origin README.md of Deep 3d Portrait from a Single Image (CVPR2020)
+This is a tensorflow implementation of the following paper: [Deep 3d Portrait from a Single Image](https://arxiv.org/abs/2004.11598). We propose a two-step geometry learning scheme which first learn 3DMM face reconstruction from single images then learn to estimate hair and ear depth in a stereo setup.
+
+## Getting Started
+### System Requirements
+- Software: Ubuntu 16.04, CUDA 9.0
+- Python  >= 3.5
+### Usage
+1. Clone the repository
+```
+git clone https://github.com/sicxu/Deep3dPortrait.git
+cd Deep3dPortrait
+pip install -r requirements.txt
+```
+2. Follow the intructions in [Deep3DFaceReconstruction](https://github.com/microsoft/Deep3DFaceReconstruction) to prepare the [BFM folder](/BFM)
+3. Download the pretrained [face reconstruction model](https://drive.google.com/file/d/1fPsvLKghlCK8rknb9GPiKwIq9HIqWWwV/view?usp=sharing) and [depth estimation model](https://drive.google.com/file/d/1QUSK4k6ZONOZWpph9GHW0VGAiKFggEU5/view?usp=sharing), then put the pb files into the [model folder](model).
+4. Run the following steps.
+
+```
+python step1_recon_3d_face.py
+python step2_face_segmentation.py
+python step3_get_head_geometry.py
+python step4_save_obj.py
+```
+
+5. To check the results, see ./output subfolders which contain the results of corresponding steps.
+
+## Others
+- An image pre-alignment is necessary for face reconstruction. We recommend using [Bulat et al.'s method](https://github.com/1adrianb/2D-and-3D-face-alignment) to get facial landmarks (3D definition). We also need to use the masks of face,  hair and ear as input to the depth estimation network. We recommend using [Lin et al.'s method](https://arxiv.org/pdf/1906.01342.pdf) for semantic segmentation.
+- The face reconstruction code is heavily borrowed from [Deep3DFaceReconstruction](https://github.com/microsoft/Deep3DFaceReconstruction).
+- The [render](utils/render) code is modified from [tf_mesh_render](https://github.com/google/tf_mesh_renderer/tree/ba27ea1798f6ee8d03ddbc52f42ab4241f9328bb). Note that the renderer we complied does not support other tensorflow versions and can only be used on Linux.
+- The manipulation code will not be released. If you want to make a comparison with our method, please use the results in our paper, or you can contact me(sicheng_xu@yeah.net) for more comparisons.
+
+## Citation
+If you find this code helpful for your research, please cite our paper.

integrated_process.py

@@ -1,47 +1,36 @@
-import os
-#from step0_68points import get_facial_landmark
-from step1_recon_3d_face import face_recon
+import os, argparse
+from step1_1_manipulate_expression import face_recon
 from step2_face_segmentation import prepare_mask
-from step3_get_head_geometry import depth_recon
-from step4_save_obj import save_obj
+from step3_1_modify_texture import depth_recon
+from step4_1_modify_texture import save_obj
 import sys
 sys.path.append('face-parsing.PyTorch')
 from step0_get_segmentation import get_face_alignment
 sys.path.remove('face-parsing.PyTorch')
-from flask import Flask, flash, request, redirect, url_for
-from flask import send_file
 
-def filemaking():
+def filemaking(input_img):
     # run function
     get_face_alignment(
-        '/workspace/FaceReenactment/Deep3DPortrait/inputs',
-        '/workspace/FaceReenactment/Deep3DPortrait/inputs',
+        './inputs',
+        './inputs',
         debug='store_true')
     os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-    face_recon('inputs', 'outputs/step1','outputs/step1/vis')
+    expr_path = 'expressions'
+    input_path = 'inputs'
+    expr_img = [i for i in os.listdir(expr_path) if (i.endswith('png') or i.endswith('jpg') or i.endswith('jpeg')) and (i[:2] != '._') ]
+    get_face_alignment(expr_path, expr_path, debug='store_true')
+    src_img = os.path.join(input_path, input_img)
+    for target in expr_img :
+        for degree in range(0, 110, 10):
+            output_name = src_img.split(os.path.sep)[1].split('.')[0] + '_' + target.split('.')[0] +'_' + str(degree) + '%'
+            face_recon(src_img, os.path.join(expr_path, target), input_path, 'outputs/step1', output_name,'outputs/step1/vis', degree=degree)
     prepare_mask('outputs/step1', 'outputs/step2', 'outputs/step1', 'outputs/step2/vis')
     depth_recon('outputs/step2', 'outputs/step3')
     save_obj('outputs/step3','outputs/step4', True)
 
-app = Flask (__name__)
-
-@app.route('/',methods = ['POST'])
-def upload_file():
-        # check if the post request has the file part
-    if 'file' not in request.files:
-        flash('No file part')
-        return redirect(request.url)
-    file = request.files['file']
-    # if user does not select file, browser also
-    # submit an empty part without filename
-    if file.filename == '':
-        flash('No selected file')
-        return redirect(request.url)
-    # print(file)
-    print(os.path.join(os.getcwd(), "inputs",file.filename))
-    file.save(os.path.join(os.getcwd(), "inputs",file.filename))
-    filemaking()
-    return send_file(os.path.join(os.getcwd(),"outputs/step4","input.obj"),as_attachment=True)
-
 if __name__ == "__main__":
-    app.run(host='0.0.0.0',port=8888)
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_img', default='AI_gen.jpg')
+    args = parser.parse_args()
+    filemaking(args.input_img)
+

step5_ui_expression&pose_change/main.py

@@ -10,18 +10,10 @@
 import PyQt5
 import sys
 from pywavefront import visualization, Wavefront
-def file_upload():
-    files = {'file': open(os.path.join(os.getcwd(),'input.png'),'rb')}
-    r = requests.post("http://s3.ciplab.ml:5443/", files=files)
-    # print(r.content.decode('utf-8'))
-    f = open("output.obj", "w")
-    f.write(r.content.decode('utf-8'))
-    f.close()
-    # print("gi")
+
 vertss=[]
 colorss = []
 class MyApp(QWidget):
-
     def __init__(self):
         self.rotation = 0
         self.d = gl.GLViewWidget()
@@ -38,9 +30,6 @@ def initUI(self):
         objectBox = QVBoxLayout()
         controlBox = QVBoxLayout()
         mainBox.addChildLayout(controlBox)
-        # objectBox.addStretch(1)
-        # objectBox.addStretch(1)
-        # controlBox.addStretch(2)
         mainBox.addLayout(objectBox)
         labels = ["Head Position", "Anger", "Contempt", "Disgust", "Fear", "Joy", "Sadness", "Surprise"]
         for i in range(8):
@@ -64,10 +53,6 @@ def initUI(self):
             hbox.addWidget(sld)
             objectBox.addLayout(hbox)
 
-        # objectBox.addWidget(sld)
-        # root_path = os.path.dirname(__file__)
-        # file_upload()
-        # exit(1)
         imgs_path = "result"
         sorted_list = sorted(os.listdir(imgs_path))
         # change the sequence on files in imgs_path
@@ -102,11 +87,6 @@ def initUI(self):
         self.d.addItem(self.m2)
         self.d.setFixedWidth(1000)
         self.d.setFixedHeight(1000)
-        # self.d.setCameraPosition(fov=45)
-        # self.d.setCameraPosition(pos=QtGui.QVector3D(0,0,0), distance=10, elevation=0, azimuth=0, rotation=45)
-        # self.d.opts["fov"]=45
-        # print(self.d.opts['vie(wport'])
-        # self.d.opts["rotation"]=(45,1,1,100)
         self.d.setCameraPosition(distance=5)
         self.d.opts["rotationMethod"]="quaternion"
         controlBox.addWidget(self.d)
@@ -116,7 +96,6 @@ def initUI(self):
         self.setGeometry(0,0,1600,1300)
         self.show()
 
-
     def rotateChange(self, value):
         q = QtGui.QQuaternion.fromEulerAngles(
                 0, value, 0
@@ -126,13 +105,11 @@ def rotateChange(self, value):
 
     def expressChanged(self, value):
         idx = (value//11)
-        # print(vertss[value])
         self.m2.setMeshData(vertexes=vertss[value],vertexColors=colorss[value])
         self.m2.meshDataChanged()
         for i in range(len(self.bars)):
             if i != idx:
                 self.bars[i].setValue(i*10)
-                # self.bars[i].update()
         self.d.update()
 
 if __name__ == '__main__':