liveportrait

src/utils/dependencies/insightface/app/__init__.py

+from .face_analysis import *

src/utils/dependencies/insightface/app/common.py

+import numpy as np
+from numpy.linalg import norm as l2norm
+#from easydict import EasyDict
+
+class Face(dict):
+
+    def __init__(self, d=None, **kwargs):
+        if d is None:
+            d = {}
+        if kwargs:
+            d.update(**kwargs)
+        for k, v in d.items():
+            setattr(self, k, v)
+        # Class attributes
+        #for k in self.__class__.__dict__.keys():
+        #    if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'):
+        #        setattr(self, k, getattr(self, k))
+
+    def __setattr__(self, name, value):
+        if isinstance(value, (list, tuple)):
+            value = [self.__class__(x)
+                    if isinstance(x, dict) else x for x in value]
+        elif isinstance(value, dict) and not isinstance(value, self.__class__):
+            value = self.__class__(value)
+        super(Face, self).__setattr__(name, value)
+        super(Face, self).__setitem__(name, value)
+
+    __setitem__ = __setattr__
+
+    def __getattr__(self, name):
+        return None
+
+    @property
+    def embedding_norm(self):
+        if self.embedding is None:
+            return None
+        return l2norm(self.embedding)
+
+    @property 
+    def normed_embedding(self):
+        if self.embedding is None:
+            return None
+        return self.embedding / self.embedding_norm
+
+    @property 
+    def sex(self):
+        if self.gender is None:
+            return None
+        return 'M' if self.gender==1 else 'F'

src/utils/dependencies/insightface/app/face_analysis.py

+# -*- coding: utf-8 -*-
+# @Organization  : insightface.ai
+# @Author        : Jia Guo
+# @Time          : 2021-05-04
+# @Function      :
+
+
+from __future__ import division
+
+import glob
+import os.path as osp
+
+import numpy as np
+import onnxruntime
+from numpy.linalg import norm
+
+from ..model_zoo import model_zoo
+from ..utils import ensure_available
+from .common import Face
+
+
+DEFAULT_MP_NAME = 'buffalo_l'
+__all__ = ['FaceAnalysis']
+
+class FaceAnalysis:
+    def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs):
+        onnxruntime.set_default_logger_severity(3)
+        self.models = {}
+        self.model_dir = ensure_available('models', name, root=root)
+        onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx'))
+        onnx_files = sorted(onnx_files)
+        for onnx_file in onnx_files:
+            model = model_zoo.get_model(onnx_file, **kwargs)
+            if model is None:
+                print('model not recognized:', onnx_file)
+            elif allowed_modules is not None and model.taskname not in allowed_modules:
+                print('model ignore:', onnx_file, model.taskname)
+                del model
+            elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
+                # print('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std)
+                self.models[model.taskname] = model
+            else:
+                print('duplicated model task type, ignore:', onnx_file, model.taskname)
+                del model
+        assert 'detection' in self.models
+        self.det_model = self.models['detection']
+
+
+    def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
+        self.det_thresh = det_thresh
+        assert det_size is not None
+        # print('set det-size:', det_size)
+        self.det_size = det_size
+        for taskname, model in self.models.items():
+            if taskname=='detection':
+                model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh)
+            else:
+                model.prepare(ctx_id)
+
+    def get(self, img, max_num=0):
+        bboxes, kpss = self.det_model.detect(img,
+                                             max_num=max_num,
+                                             metric='default')
+        if bboxes.shape[0] == 0:
+            return []
+        ret = []
+        for i in range(bboxes.shape[0]):
+            bbox = bboxes[i, 0:4]
+            det_score = bboxes[i, 4]
+            kps = None
+            if kpss is not None:
+                kps = kpss[i]
+            face = Face(bbox=bbox, kps=kps, det_score=det_score)
+            for taskname, model in self.models.items():
+                if taskname=='detection':
+                    continue
+                model.get(img, face)
+            ret.append(face)
+        return ret
+
+    def draw_on(self, img, faces):
+        import cv2
+        dimg = img.copy()
+        for i in range(len(faces)):
+            face = faces[i]
+            box = face.bbox.astype(np.int)
+            color = (0, 0, 255)
+            cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
+            if face.kps is not None:
+                kps = face.kps.astype(np.int)
+                #print(landmark.shape)
+                for l in range(kps.shape[0]):
+                    color = (0, 0, 255)
+                    if l == 0 or l == 3:
+                        color = (0, 255, 0)
+                    cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color,
+                               2)
+            if face.gender is not None and face.age is not None:
+                cv2.putText(dimg,'%s,%d'%(face.sex,face.age), (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1)
+
+            #for key, value in face.items():
+            #    if key.startswith('landmark_3d'):
+            #        print(key, value.shape)
+            #        print(value[0:10,:])
+            #        lmk = np.round(value).astype(np.int)
+            #        for l in range(lmk.shape[0]):
+            #            color = (255, 0, 0)
+            #            cv2.circle(dimg, (lmk[l][0], lmk[l][1]), 1, color,
+            #                       2)
+        return dimg

src/utils/dependencies/insightface/data/__init__.py

+from .image import get_image
+from .pickle_object import get_object

src/utils/dependencies/insightface/data/image.py

+import cv2
+import os
+import os.path as osp
+from pathlib import Path
+
+class ImageCache:
+    data = {}
+
+def get_image(name, to_rgb=False):
+    key = (name, to_rgb)
+    if key in ImageCache.data:
+        return ImageCache.data[key]
+    images_dir = osp.join(Path(__file__).parent.absolute(), 'images')
+    ext_names = ['.jpg', '.png', '.jpeg']
+    image_file = None
+    for ext_name in ext_names:
+        _image_file = osp.join(images_dir, "%s%s"%(name, ext_name))
+        if osp.exists(_image_file):
+            image_file = _image_file
+            break
+    assert image_file is not None, '%s not found'%name
+    img = cv2.imread(image_file)
+    if to_rgb:
+        img = img[:,:,::-1]
+    ImageCache.data[key] = img
+    return img
+

src/utils/dependencies/insightface/data/pickle_object.py

+import cv2
+import os
+import os.path as osp
+from pathlib import Path
+import pickle
+
+def get_object(name):
+    objects_dir = osp.join(Path(__file__).parent.absolute(), 'objects')
+    if not name.endswith('.pkl'):
+        name = name+".pkl"
+    filepath = osp.join(objects_dir, name)
+    if not osp.exists(filepath):
+        return None
+    with open(filepath, 'rb') as f:
+        obj = pickle.load(f)
+    return obj
+

src/utils/dependencies/insightface/data/rec_builder.py

+import pickle
+import numpy as np
+import os
+import os.path as osp
+import sys
+import mxnet as mx
+
+
+class RecBuilder():
+    def __init__(self, path, image_size=(112, 112)):
+        self.path = path
+        self.image_size = image_size
+        self.widx = 0
+        self.wlabel = 0
+        self.max_label = -1
+        assert not osp.exists(path), '%s exists' % path
+        os.makedirs(path)
+        self.writer = mx.recordio.MXIndexedRecordIO(os.path.join(path, 'train.idx'), 
+                                                    os.path.join(path, 'train.rec'),
+                                                    'w')
+        self.meta = []
+
+    def add(self, imgs):
+        #!!! img should be BGR!!!!
+        #assert label >= 0
+        #assert label > self.last_label
+        assert len(imgs) > 0
+        label = self.wlabel
+        for img in imgs:
+            idx = self.widx
+            image_meta = {'image_index': idx, 'image_classes': [label]}
+            header = mx.recordio.IRHeader(0, label, idx, 0)
+            if isinstance(img, np.ndarray):
+                s = mx.recordio.pack_img(header,img,quality=95,img_fmt='.jpg')
+            else:
+                s = mx.recordio.pack(header, img)
+            self.writer.write_idx(idx, s)
+            self.meta.append(image_meta)
+            self.widx += 1
+        self.max_label = label
+        self.wlabel += 1
+
+
+    def add_image(self, img, label):
+        #!!! img should be BGR!!!!
+        #assert label >= 0
+        #assert label > self.last_label
+        idx = self.widx
+        header = mx.recordio.IRHeader(0, label, idx, 0)
+        if isinstance(label, list):
+            idlabel = label[0]
+        else:
+            idlabel = label
+        image_meta = {'image_index': idx, 'image_classes': [idlabel]}
+        if isinstance(img, np.ndarray):
+            s = mx.recordio.pack_img(header,img,quality=95,img_fmt='.jpg')
+        else:
+            s = mx.recordio.pack(header, img)
+        self.writer.write_idx(idx, s)
+        self.meta.append(image_meta)
+        self.widx += 1
+        self.max_label = max(self.max_label, idlabel)
+
+    def close(self):
+        with open(osp.join(self.path, 'train.meta'), 'wb') as pfile:
+            pickle.dump(self.meta, pfile, protocol=pickle.HIGHEST_PROTOCOL)
+        print('stat:', self.widx, self.wlabel)
+        with open(os.path.join(self.path, 'property'), 'w') as f:
+            f.write("%d,%d,%d\n" % (self.max_label+1, self.image_size[0], self.image_size[1]))
+            f.write("%d\n" % (self.widx))
+

src/utils/dependencies/insightface/model_zoo/__init__.py

+from .model_zoo import get_model
+from .arcface_onnx import ArcFaceONNX
+from .retinaface import RetinaFace
+from .scrfd import SCRFD
+from .landmark import Landmark
+from .attribute import Attribute

src/utils/dependencies/insightface/model_zoo/arcface_onnx.py

+# -*- coding: utf-8 -*-
+# @Organization  : insightface.ai
+# @Author        : Jia Guo
+# @Time          : 2021-05-04
+# @Function      : 
+
+from __future__ import division
+import numpy as np
+import cv2
+import onnx
+import onnxruntime
+from ..utils import face_align
+
+__all__ = [
+    'ArcFaceONNX',
+]
+
+
+class ArcFaceONNX:
+    def __init__(self, model_file=None, session=None):
+        assert model_file is not None
+        self.model_file = model_file
+        self.session = session
+        self.taskname = 'recognition'
+        find_sub = False
+        find_mul = False
+        model = onnx.load(self.model_file)
+        graph = model.graph
+        for nid, node in enumerate(graph.node[:8]):
+            #print(nid, node.name)
+            if node.name.startswith('Sub') or node.name.startswith('_minus'):
+                find_sub = True
+            if node.name.startswith('Mul') or node.name.startswith('_mul'):
+                find_mul = True
+        if find_sub and find_mul:
+            #mxnet arcface model
+            input_mean = 0.0
+            input_std = 1.0
+        else:
+            input_mean = 127.5
+            input_std = 127.5
+        self.input_mean = input_mean
+        self.input_std = input_std
+        #print('input mean and std:', self.input_mean, self.input_std)
+        if self.session is None:
+            self.session = onnxruntime.InferenceSession(self.model_file, None)
+        input_cfg = self.session.get_inputs()[0]
+        input_shape = input_cfg.shape
+        input_name = input_cfg.name
+        self.input_size = tuple(input_shape[2:4][::-1])
+        self.input_shape = input_shape
+        outputs = self.session.get_outputs()
+        output_names = []
+        for out in outputs:
+            output_names.append(out.name)
+        self.input_name = input_name
+        self.output_names = output_names
+        assert len(self.output_names)==1
+        self.output_shape = outputs[0].shape
+
+    def prepare(self, ctx_id, **kwargs):
+        if ctx_id<0:
+            self.session.set_providers(['CPUExecutionProvider'])
+
+    def get(self, img, face):
+        aimg = face_align.norm_crop(img, landmark=face.kps, image_size=self.input_size[0])
+        face.embedding = self.get_feat(aimg).flatten()
+        return face.embedding
+
+    def compute_sim(self, feat1, feat2):
+        from numpy.linalg import norm
+        feat1 = feat1.ravel()
+        feat2 = feat2.ravel()
+        sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2))
+        return sim
+
+    def get_feat(self, imgs):
+        if not isinstance(imgs, list):
+            imgs = [imgs]
+        input_size = self.input_size
+        
+        blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size,
+                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        net_out = self.session.run(self.output_names, {self.input_name: blob})[0]
+        return net_out
+
+    def forward(self, batch_data):
+        blob = (batch_data - self.input_mean) / self.input_std
+        net_out = self.session.run(self.output_names, {self.input_name: blob})[0]
+        return net_out
+
+

src/utils/dependencies/insightface/model_zoo/attribute.py

+# -*- coding: utf-8 -*-
+# @Organization  : insightface.ai
+# @Author        : Jia Guo
+# @Time          : 2021-06-19
+# @Function      : 
+
+from __future__ import division
+import numpy as np
+import cv2
+import onnx
+import onnxruntime
+from ..utils import face_align
+
+__all__ = [
+    'Attribute',
+]
+
+
+class Attribute:
+    def __init__(self, model_file=None, session=None):
+        assert model_file is not None
+        self.model_file = model_file
+        self.session = session
+        find_sub = False
+        find_mul = False
+        model = onnx.load(self.model_file)
+        graph = model.graph
+        for nid, node in enumerate(graph.node[:8]):
+            #print(nid, node.name)
+            if node.name.startswith('Sub') or node.name.startswith('_minus'):
+                find_sub = True
+            if node.name.startswith('Mul') or node.name.startswith('_mul'):
+                find_mul = True
+            if nid<3 and node.name=='bn_data':
+                find_sub = True
+                find_mul = True
+        if find_sub and find_mul:
+            #mxnet arcface model
+            input_mean = 0.0
+            input_std = 1.0
+        else:
+            input_mean = 127.5
+            input_std = 128.0
+        self.input_mean = input_mean
+        self.input_std = input_std
+        #print('input mean and std:', model_file, self.input_mean, self.input_std)
+        if self.session is None:
+            self.session = onnxruntime.InferenceSession(self.model_file, None)
+        input_cfg = self.session.get_inputs()[0]
+        input_shape = input_cfg.shape
+        input_name = input_cfg.name
+        self.input_size = tuple(input_shape[2:4][::-1])
+        self.input_shape = input_shape
+        outputs = self.session.get_outputs()
+        output_names = []
+        for out in outputs:
+            output_names.append(out.name)
+        self.input_name = input_name
+        self.output_names = output_names
+        assert len(self.output_names)==1
+        output_shape = outputs[0].shape
+        #print('init output_shape:', output_shape)
+        if output_shape[1]==3:
+            self.taskname = 'genderage'
+        else:
+            self.taskname = 'attribute_%d'%output_shape[1]
+
+    def prepare(self, ctx_id, **kwargs):
+        if ctx_id<0:
+            self.session.set_providers(['CPUExecutionProvider'])
+
+    def get(self, img, face):
+        bbox = face.bbox
+        w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+        center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+        rotate = 0
+        _scale = self.input_size[0]  / (max(w, h)*1.5)
+        #print('param:', img.shape, bbox, center, self.input_size, _scale, rotate)
+        aimg, M = face_align.transform(img, center, self.input_size[0], _scale, rotate)
+        input_size = tuple(aimg.shape[0:2][::-1])
+        #assert input_size==self.input_size
+        blob = cv2.dnn.blobFromImage(aimg, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        pred = self.session.run(self.output_names, {self.input_name : blob})[0][0]
+        if self.taskname=='genderage':
+            assert len(pred)==3
+            gender = np.argmax(pred[:2])
+            age = int(np.round(pred[2]*100))
+            face['gender'] = gender
+            face['age'] = age
+            return gender, age
+        else:
+            return pred
+
+

src/utils/dependencies/insightface/model_zoo/inswapper.py

+import time
+import numpy as np
+import onnxruntime
+import cv2
+import onnx
+from onnx import numpy_helper
+from ..utils import face_align
+
+
+
+
+class INSwapper():
+    def __init__(self, model_file=None, session=None):
+        self.model_file = model_file
+        self.session = session
+        model = onnx.load(self.model_file)
+        graph = model.graph
+        self.emap = numpy_helper.to_array(graph.initializer[-1])
+        self.input_mean = 0.0
+        self.input_std = 255.0
+        #print('input mean and std:', model_file, self.input_mean, self.input_std)
+        if self.session is None:
+            self.session = onnxruntime.InferenceSession(self.model_file, None)
+        inputs = self.session.get_inputs()
+        self.input_names = []
+        for inp in inputs:
+            self.input_names.append(inp.name)
+        outputs = self.session.get_outputs()
+        output_names = []
+        for out in outputs:
+            output_names.append(out.name)
+        self.output_names = output_names
+        assert len(self.output_names)==1
+        output_shape = outputs[0].shape
+        input_cfg = inputs[0]
+        input_shape = input_cfg.shape
+        self.input_shape = input_shape
+        # print('inswapper-shape:', self.input_shape)
+        self.input_size = tuple(input_shape[2:4][::-1])
+
+    def forward(self, img, latent):
+        img = (img - self.input_mean) / self.input_std
+        pred = self.session.run(self.output_names, {self.input_names[0]: img, self.input_names[1]: latent})[0]
+        return pred
+
+    def get(self, img, target_face, source_face, paste_back=True):
+        face_mask = np.zeros((img.shape[0], img.shape[1]), np.uint8)
+        cv2.fillPoly(face_mask, np.array([target_face.landmark_2d_106[[1,9,10,11,12,13,14,15,16,2,3,4,5,6,7,8,0,24,23,22,21,20,19,18,32,31,30,29,28,27,26,25,17,101,105,104,103,51,49,48,43]].astype('int64')]), 1)
+        aimg, M = face_align.norm_crop2(img, target_face.kps, self.input_size[0])
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size,
+                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        latent = source_face.normed_embedding.reshape((1,-1))
+        latent = np.dot(latent, self.emap)
+        latent /= np.linalg.norm(latent)
+        pred = self.session.run(self.output_names, {self.input_names[0]: blob, self.input_names[1]: latent})[0]
+        #print(latent.shape, latent.dtype, pred.shape)
+        img_fake = pred.transpose((0,2,3,1))[0]
+        bgr_fake = np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:,:,::-1]
+        if not paste_back:
+            return bgr_fake, M
+        else:
+            target_img = img
+            fake_diff = bgr_fake.astype(np.float32) - aimg.astype(np.float32)
+            fake_diff = np.abs(fake_diff).mean(axis=2)
+            fake_diff[:2,:] = 0
+            fake_diff[-2:,:] = 0
+            fake_diff[:,:2] = 0
+            fake_diff[:,-2:] = 0
+            IM = cv2.invertAffineTransform(M)
+            img_white = np.full((aimg.shape[0],aimg.shape[1]), 255, dtype=np.float32)
+            bgr_fake = cv2.warpAffine(bgr_fake, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+            img_white = cv2.warpAffine(img_white, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+            fake_diff = cv2.warpAffine(fake_diff, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+            img_white[img_white>20] = 255
+            fthresh = 10
+            fake_diff[fake_diff<fthresh] = 0
+            fake_diff[fake_diff>=fthresh] = 255
+            img_mask = img_white
+            mask_h_inds, mask_w_inds = np.where(img_mask==255)
+            mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
+            mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
+            mask_size = int(np.sqrt(mask_h*mask_w))
+            k = max(mask_size//10, 10)
+            #k = max(mask_size//20, 6)
+            #k = 6
+            kernel = np.ones((k,k),np.uint8)
+            img_mask = cv2.erode(img_mask,kernel,iterations = 1)
+            kernel = np.ones((2,2),np.uint8)
+            fake_diff = cv2.dilate(fake_diff,kernel,iterations = 1)
+
+            face_mask = cv2.erode(face_mask,np.ones((11,11),np.uint8),iterations = 1)
+            fake_diff[face_mask==1] = 255
+
+            k = max(mask_size//20, 5)
+            #k = 3
+            #k = 3
+            kernel_size = (k, k)
+            blur_size = tuple(2*i+1 for i in kernel_size)
+            img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+            k = 5
+            kernel_size = (k, k)
+            blur_size = tuple(2*i+1 for i in kernel_size)
+            fake_diff = cv2.blur(fake_diff, (11,11), 0)
+            ##fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0)
+            # print('blur_size: ', blur_size)
+            # fake_diff = cv2.blur(fake_diff, (21, 21), 0) # blur_size
+            img_mask /= 255
+            fake_diff /= 255
+            # img_mask = fake_diff
+            img_mask = img_mask*fake_diff
+            img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
+            fake_merged = img_mask * bgr_fake + (1-img_mask) * target_img.astype(np.float32)
+            fake_merged = fake_merged.astype(np.uint8)
+            return fake_merged

src/utils/dependencies/insightface/model_zoo/landmark.py

+# -*- coding: utf-8 -*-
+# @Organization  : insightface.ai
+# @Author        : Jia Guo
+# @Time          : 2021-05-04
+# @Function      : 
+
+from __future__ import division
+import numpy as np
+import cv2
+import onnx
+import onnxruntime
+from ..utils import face_align
+from ..utils import transform
+from ..data import get_object
+
+__all__ = [
+    'Landmark',
+]
+
+
+class Landmark:
+    def __init__(self, model_file=None, session=None):
+        assert model_file is not None
+        self.model_file = model_file
+        self.session = session
+        find_sub = False
+        find_mul = False
+        model = onnx.load(self.model_file)
+        graph = model.graph
+        for nid, node in enumerate(graph.node[:8]):
+            #print(nid, node.name)
+            if node.name.startswith('Sub') or node.name.startswith('_minus'):
+                find_sub = True
+            if node.name.startswith('Mul') or node.name.startswith('_mul'):
+                find_mul = True
+            if nid<3 and node.name=='bn_data':
+                find_sub = True
+                find_mul = True
+        if find_sub and find_mul:
+            #mxnet arcface model
+            input_mean = 0.0
+            input_std = 1.0
+        else:
+            input_mean = 127.5
+            input_std = 128.0
+        self.input_mean = input_mean
+        self.input_std = input_std
+        #print('input mean and std:', model_file, self.input_mean, self.input_std)
+        if self.session is None:
+            self.session = onnxruntime.InferenceSession(self.model_file, None)
+        input_cfg = self.session.get_inputs()[0]
+        input_shape = input_cfg.shape
+        input_name = input_cfg.name
+        self.input_size = tuple(input_shape[2:4][::-1])
+        self.input_shape = input_shape
+        outputs = self.session.get_outputs()
+        output_names = []
+        for out in outputs:
+            output_names.append(out.name)
+        self.input_name = input_name
+        self.output_names = output_names
+        assert len(self.output_names)==1
+        output_shape = outputs[0].shape
+        self.require_pose = False
+        #print('init output_shape:', output_shape)
+        if output_shape[1]==3309:
+            self.lmk_dim = 3
+            self.lmk_num = 68
+            self.mean_lmk = get_object('meanshape_68.pkl')
+            self.require_pose = True
+        else:
+            self.lmk_dim = 2
+            self.lmk_num = output_shape[1]//self.lmk_dim
+        self.taskname = 'landmark_%dd_%d'%(self.lmk_dim, self.lmk_num)
+
+    def prepare(self, ctx_id, **kwargs):
+        if ctx_id<0:
+            self.session.set_providers(['CPUExecutionProvider'])
+
+    def get(self, img, face):
+        bbox = face.bbox
+        w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+        center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+        rotate = 0
+        _scale = self.input_size[0]  / (max(w, h)*1.5)
+        #print('param:', img.shape, bbox, center, self.input_size, _scale, rotate)
+        aimg, M = face_align.transform(img, center, self.input_size[0], _scale, rotate)
+        input_size = tuple(aimg.shape[0:2][::-1])
+        #assert input_size==self.input_size
+        blob = cv2.dnn.blobFromImage(aimg, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        pred = self.session.run(self.output_names, {self.input_name : blob})[0][0]
+        if pred.shape[0] >= 3000:
+            pred = pred.reshape((-1, 3))
+        else:
+            pred = pred.reshape((-1, 2))
+        if self.lmk_num < pred.shape[0]:
+            pred = pred[self.lmk_num*-1:,:]
+        pred[:, 0:2] += 1
+        pred[:, 0:2] *= (self.input_size[0] // 2)
+        if pred.shape[1] == 3:
+            pred[:, 2] *= (self.input_size[0] // 2)
+
+        IM = cv2.invertAffineTransform(M)
+        pred = face_align.trans_points(pred, IM)
+        face[self.taskname] = pred
+        if self.require_pose:
+            P = transform.estimate_affine_matrix_3d23d(self.mean_lmk, pred)
+            s, R, t = transform.P2sRt(P)
+            rx, ry, rz = transform.matrix2angle(R)
+            pose = np.array( [rx, ry, rz], dtype=np.float32 )
+            face['pose'] = pose #pitch, yaw, roll
+        return pred
+
+

src/utils/dependencies/insightface/model_zoo/model_store.py

+"""
+This code file mainly comes from https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/model_store.py
+"""
+from __future__ import print_function
+
+__all__ = ['get_model_file']
+import os
+import zipfile
+import glob
+
+from ..utils import download, check_sha1
+
+_model_sha1 = {
+    name: checksum
+    for checksum, name in [
+        ('95be21b58e29e9c1237f229dae534bd854009ce0', 'arcface_r100_v1'),
+        ('', 'arcface_mfn_v1'),
+        ('39fd1e087a2a2ed70a154ac01fecaa86c315d01b', 'retinaface_r50_v1'),
+        ('2c9de8116d1f448fd1d4661f90308faae34c990a', 'retinaface_mnet025_v1'),
+        ('0db1d07921d005e6c9a5b38e059452fc5645e5a4', 'retinaface_mnet025_v2'),
+        ('7dd8111652b7aac2490c5dcddeb268e53ac643e6', 'genderage_v1'),
+    ]
+}
+
+base_repo_url = 'https://insightface.ai/files/'
+_url_format = '{repo_url}models/{file_name}.zip'
+
+
+def short_hash(name):
+    if name not in _model_sha1:
+        raise ValueError(
+            'Pretrained model for {name} is not available.'.format(name=name))
+    return _model_sha1[name][:8]
+
+
+def find_params_file(dir_path):
+    if not os.path.exists(dir_path):
+        return None
+    paths = glob.glob("%s/*.params" % dir_path)
+    if len(paths) == 0:
+        return None
+    paths = sorted(paths)
+    return paths[-1]
+
+
+def get_model_file(name, root=os.path.join('~', '.insightface', 'models')):
+    r"""Return location for the pretrained on local file system.
+
+    This function will download from online model zoo when model cannot be found or has mismatch.
+    The root directory will be created if it doesn't exist.
+
+    Parameters
+    ----------
+    name : str
+        Name of the model.
+    root : str, default '~/.mxnet/models'
+        Location for keeping the model parameters.
+
+    Returns
+    -------
+    file_path
+        Path to the requested pretrained model file.
+    """
+
+    file_name = name
+    root = os.path.expanduser(root)
+    dir_path = os.path.join(root, name)
+    file_path = find_params_file(dir_path)
+    #file_path = os.path.join(root, file_name + '.params')
+    sha1_hash = _model_sha1[name]
+    if file_path is not None:
+        if check_sha1(file_path, sha1_hash):
+            return file_path
+        else:
+            print(
+                'Mismatch in the content of model file detected. Downloading again.'
+            )
+    else:
+        print('Model file is not found. Downloading.')
+
+    if not os.path.exists(root):
+        os.makedirs(root)
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+    zip_file_path = os.path.join(root, file_name + '.zip')
+    repo_url = base_repo_url
+    if repo_url[-1] != '/':
+        repo_url = repo_url + '/'
+    download(_url_format.format(repo_url=repo_url, file_name=file_name),
+             path=zip_file_path,
+             overwrite=True)
+    with zipfile.ZipFile(zip_file_path) as zf:
+        zf.extractall(dir_path)
+    os.remove(zip_file_path)
+    file_path = find_params_file(dir_path)
+
+    if check_sha1(file_path, sha1_hash):
+        return file_path
+    else:
+        raise ValueError(
+            'Downloaded file has different hash. Please try again.')
+