def geneDataTxt(imgPath,landmarkREF,mode = 'test'):
    
    imgTxt = 'dataset/data/testImageList.txt'
    data = getDataFromTxt(imgTxt,False,False)  #image_path,bbox
    testData = defaultdict(lambda:dict(landmarks=[],patches=[],label=[]))
    logger("generate 25 positive samples and 500 negative samples for per landmark")
    
    testData = generateSamples(testData,data,landmarkREF)
    
    for idx,name in types:
        patches = np.asarray(testData[name]['patches'])
        landmarks = np.asarray(testData[name]['landmarks'])
        #label = np.asarray(testData[name]['label'])

        patches = processImage(patches)
        shuffle_in_unison_scary(patches,landmarks)
        
        createDir('dataset/test/%s' % imgPath[-7:-4])
        
        with h5py.File('dataset/test/%s/%s.h5' % (imgPath[-7:-4],name),'w') as h5:
            h5['data'] = patches.astype(np.float32)
            h5['landmark'] = landmarks.astype(np.float32)
            #h5['label'] = label.astype(np.uint8)
        with open('dataset/test/%s/%s.txt' % (imgPath[-7:-4],name),'w') as fd:
            fd.write('dataset/test/%s/%s.h5'% (imgPath[-7:-4],name))
        
        '''with open('dataset/test/%s.txt' % (name),'w') as fd:
def geneDataTxt(imgPath, landmarks):

    imgTxt = 'dataset/data/testImageList.txt'
    data = getDataFromTxt(imgTxt, False, False)  #image_path,bbox
    trainData = defaultdict(lambda: dict(landmarks=[], patches=[], label=[]))
    logger(
        "generate 25 positive samples and 500 negative samples for per landmark"
    )

    trainData = generateSamples(trainData, data, landmarks)

    arr = []
    for idx, name in types:
        patches = np.asarray(trainData[name]['patches'])
        landmarks = np.asarray(trainData[name]['landmarks'])
        label = np.asarray(trainData[name]['label'])

        arr1 = landmarks.reshape(1, -1)
        arr.append(arr1)

        patches = processImage(patches)
        shuffle_in_unison_scary(patches, landmarks, label)

        with open('test/%s.txt' % (name), 'w') as fd:
            fd.append(landmarks.astype(np.float32))
            fd.append(patches.astype(np.float32))
            fd.append(label.astype(np.uint8))
Ejemplo n.º 3
0
def load_all_shoes():
    conn = MongoClient(databaseUri)
    db = conn.getter
    shoes = []
    labels = []
    ids = []
    docs = db.items.find(
        {
            "detail.images": {
                "$elemMatch": {
                    "_id": {
                        "$exists": True
                    }
                }
            }
        }, {
            "detail": 1
        }).limit(shoe_limit)
    fs = []
    for doc in docs:
        if len(doc["detail"]["images"]) == 7:
            for image in doc["detail"]["images"]:
                if 'z' in image and image[
                        "z"] == 90 and 'y' in image and image["y"] == 0:

                    info = {
                        "f":
                        image_source + image["path"],
                        "cat":
                        doc["detail"]["categories"][
                            len(doc["detail"]["categories"]) - 1],
                        "image_id":
                        image["_id"]
                    }
                    try:
                        fs.append(info)
                    except:
                        print info
                        print doc["_id"]["_id"]

    counter = 0
    for i in fs:

        # cat = doc["shoe"]["categories"][len(doc["shoe"]["categories"]) - 1]
        # f = "/getter_data/images/" + str(image["_id"]) + contentTypeExtension[image["content-type"]]

        print "Processing file [%d / %d] \r" % (counter, len(fs))
        counter += 1
        print i
        shoes.append(processImage(i["f"], auto_crop, SZ))
        labels.append(shoeCategory[i["cat"]])
        ids.append(i["image_id"])
        # except Exception, e:
        #             print 'action=processingFile,_id=%s,file=%s,error=%s' % (doc["_id"]["_id"], f, e)
        #             raise e
        #

    print "datasetSize=%d" % (len(shoes))
    return np.array(shoes), np.array(labels), np.array(ids)
Ejemplo n.º 4
0
def getResult(img,bbox):
    # F
    F,EN,NM = getCNNs(level = 1)
    f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
    f_face = img[f_bbox.top:f_bbox.bottom+1,f_bbox.left:f_bbox.right+1]


    f_face = cv2.resize(f_face, (39, 39))
    en_face = f_face[:31, :]
    nm_face = f_face[8:, :]

    f_face = f_face.reshape((1, 1, 39, 39))
    f_face = processImage(f_face)
    f = F.forward(f_face)
  
    # EN
    # en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
    # en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]

    en_face = cv2.resize(en_face, (31, 39)).reshape((1, 1, 31, 39))
    #en_landmark = (landmarkGt[:3, :] - landmark_pre[:3,:]).reshape((6))
    en_face = processImage(en_face)
    en = EN.forward(en_face)

    #EN_imgs.append(en_face)
    #EN_landmarks.append(en_landmark)

    # NM
    # nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
    # nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]

    nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 1, 31, 39))
    #nm_landmark = (landmarkGt[2:, :] - landmark_pre[2:,:]).reshape((6))
    nm_face = processImage(nm_face)
    nm = NM.forward(nm_face)
    #NM_imgs.append(nm_face)
    #NM_landmarks.append(nm_landmark)
    landmark = np.zeros((5, 2))
    landmark[0] = (f[0]+en[0]) / 2
    landmark[1] = (f[1]+en[1]) / 2
    landmark[2] = (f[2]+en[2]+nm[0]) / 3
    landmark[3] = (f[3]+nm[1]) / 2
    landmark[4] = (f[4]+nm[2]) / 2
    return landmark
Ejemplo n.º 5
0
def load_all_shoes():
  conn = MongoClient(databaseUri)
  db = conn.getter
  shoes = []
  labels = []
  ids = []
  docs = db.items.find({
      "detail.images": {
        "$elemMatch" : {
          "_id": {
            "$exists": True
          }
        }
      }
    },
    {
      "detail": 1
    }
  ).limit(shoe_limit)
  fs = []
  for doc in docs:
    if len(doc["detail"]["images"]) == 7:
      for image in doc["detail"]["images"]:
        if 'z' in image and image["z"] == 90 and 'y' in image and image["y"] == 0:

          info = {
            "f": image_source + image["path"],
            "cat": doc["detail"]["categories"][len(doc["detail"]["categories"]) - 1]  ,
            "image_id": image["_id"]
          }
          try:
            fs.append(info)
          except:
            print info
            print doc["_id"]["_id"]

  counter = 0;
  for i in fs:

          # cat = doc["shoe"]["categories"][len(doc["shoe"]["categories"]) - 1]
          # f = "/getter_data/images/" + str(image["_id"]) + contentTypeExtension[image["content-type"]]

    print "Processing file [%d / %d] \r" % (counter, len(fs))
    counter += 1
    print i
    shoes.append(processImage(i["f"], auto_crop, SZ))
    labels.append(shoeCategory[i["cat"]])
    ids.append(i["image_id"])
          # except Exception, e:
          #             print 'action=processingFile,_id=%s,file=%s,error=%s' % (doc["_id"]["_id"], f, e)
          #             raise e
          #


  print "datasetSize=%d" % (len(shoes))
  return np.array(shoes), np.array(labels), np.array(ids)
Ejemplo n.º 6
0
def NM(img, bbox):
    """
        LEVEL-1, NM
        img: gray image
        bbox: bounding box of face
    """
    bbox = bbox.expand(0.05)
    face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
    face = cv2.resize(face, (39, 39)).reshape((1, 1, 39, 39))
    face = processImage(face)

    F, EN, NM = getCNNs(level=1)  # TODO more flexible load needed.
    landmark = NM.forward(face[:, :, 8:, :])
    return landmark
Ejemplo n.º 7
0
def NM(img, bbox):
    """
        LEVEL-1, NM
        img: gray image
        bbox: bounding box of face
    """
    bbox = bbox.expand(0.05)
    face = img[bbox.top:bbox.bottom+1,bbox.left:bbox.right+1]
    face = cv2.resize(face, (39, 39)).reshape((1, 1, 39, 39))
    face = processImage(face)

    F, EN, NM = getCNNs(level=1) # TODO more flexible load needed.
    landmark = NM.forward(face[:, :, 8:, :])
    return landmark
Ejemplo n.º 8
0
def generate(ftxt, mode, argument=False):
    '''
    第二阶段数据源制作
    :param ftxt: 数据源文件位置和label
    :param mode: 训练或测试
    :param argument:
    :return:
    '''
    data = getDataFromTxt(ftxt)

    trainData = defaultdict(lambda: dict(patches=[], landmarks=[]))
    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)
        assert (img is not None)
        logger("process %s" % imgPath)

        landmarkPs = randomShiftWithArgument(landmarkGt, 0.05)
        if not argument:
            landmarkPs = [landmarkPs[0]]

        for landmarkP in landmarkPs:
            for idx, name, padding in types:
                patch, patch_bbox = getPatch(img, bbox, landmarkP[idx],
                                             padding)
                patch = cv2.resize(patch, (15, 15))
                patch = patch.reshape((1, 15, 15))
                trainData[name]['patches'].append(patch)
                _ = patch_bbox.project(bbox.reproject(landmarkGt[idx]))
                trainData[name]['landmarks'].append(_)

    for idx, name, padding in types:
        logger('writing training data of %s' % name)
        patches = np.asarray(trainData[name]['patches'])
        landmarks = np.asarray(trainData[name]['landmarks'])
        patches = processImage(patches)

        shuffle_in_unison_scary(patches, landmarks)

        with h5py.File(
                '/python/face_key_point/data_hdf5/train/2_%s/%s.h5' %
            (name, mode), 'w') as h5:
            h5['data'] = patches.astype(np.float32)
            h5['landmark'] = landmarks.astype(np.float32)
        with open(
                '/python/face_key_point/data_hdf5/train/2_%s/%s.txt' %
            (name, mode), 'w') as fd:
            fd.write('/python/face_key_point/data_hdf5/train/2_%s/%s.h5' %
                     (name, mode))
Ejemplo n.º 9
0
def getResult(img,bbox):
    # F
    F,EN,NM = getCNNs(level = 1)
    f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
    f_face = img[f_bbox.top:f_bbox.bottom+1,f_bbox.left:f_bbox.right+1]


    f_face = cv2.resize(f_face, (39, 39))
    en_face = f_face[:31, :]
    nm_face = f_face[8:, :]

    f_face = f_face.reshape((1, 1, 39, 39))
    f_face = processImage(f_face)
    f = F.forward(f_face)
  
    return f
Ejemplo n.º 10
0
def generate(ftxt, mode, argument=False):
    """
        Generate Training Data for LEVEL-3
        mode = train or test
    """
    data = getDataFromTxt(ftxt)

    trainData = defaultdict(lambda: dict(patches=[], landmarks=[]))
    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
        assert (img is not None)
        logger("process %s" % imgPath)

        landmarkPs = randomShiftWithArgument(landmarkGt, 0.01)
        if not argument:
            landmarkPs = [landmarkPs[0]]

        for landmarkP in landmarkPs:
            for idx, name, padding in types:
                patch, patch_bbox = getPatch(img, bbox, landmarkP[idx],
                                             padding)
                patch = cv2.resize(patch, (15, 15))
                patch = patch.reshape((1, 15, 15))
                trainData[name]['patches'].append(patch)
                _ = patch_bbox.project(bbox.reproject(landmarkGt[idx]))
                trainData[name]['landmarks'].append(_)

    for idx, name, padding in types:
        logger('writing training data of %s' % name)
        patches = np.asarray(trainData[name]['patches'])
        landmarks = np.asarray(trainData[name]['landmarks'])
        patches = processImage(patches)

        shuffle_in_unison_scary(patches, landmarks)

        with h5py.File(
                '/home/tyd/下载/deep_landmark/mydataset/mytrain/3_%s/%s.h5' %
            (name, mode), 'w') as h5:
            h5['data'] = patches.astype(np.float32)
            h5['landmark'] = landmarks.astype(np.float32)
        with open(
                '/home/tyd/下载/deep_landmark/mydataset/mytrain/3_%s/%s.txt' %
            (name, mode), 'w') as fd:
            fd.write(
                '/home/tyd/下载/deep_landmark/mydataset/mytrain/3_%s/%s.h5' %
                (name, mode))
Ejemplo n.º 11
0
def generate_hdf5():  
      
    labelfile =readdata('../data/my_alige_landmark.txt')  
    F_imgs = []  
    F_landmarks = []  
      
      
    for i,l in enumerate(labelfile):  
        imgpath='../data/'+l[0]  
      
        img=cv2.imread(imgpath)  
        maxx=max(img.shape[0],img.shape[1])  
        img=sqrtimg(img)#把输入图片填充成正方形,因为我们要训练的图片的大小是正方形的图片255*255  
        img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#图片转为灰度图像  
        f_face=cv2.resize(img,(39,39))#把图片缩放成255*255的图片  
        # F  
        plt.imshow(f_face,cmap='gray')  
      
      
        f_face = f_face.reshape((1, 39, 39))  
        f_landmark =np.asarray(l[1:],dtype='float')  
   
        F_imgs.append(f_face)  
      
      
        #归一化人脸特征点标签,因为上面height等于width,这里比较懒,直接简写  
        f_landmark=f_landmark/maxx #归一化到0~1之间  
        print f_landmark  
        F_landmarks.append(f_landmark)  
      
      
    F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)  
      
      
    F_imgs = processImage(F_imgs)#图片预处理,包含均值归一化,方差归一化等  
    shuffle_in_unison_scary(F_imgs, F_landmarks)#打乱数据  
      
    #生成h5py格式  
    with h5py.File(os.getcwd()+ '/train_data.h5', 'w') as f:  
    f['data'] = F_imgs.astype(np.float32)  
        f['landmark'] = F_landmarks.astype(np.float32)  
    #因为caffe的输入h5py不是直接使用上面的数据,而是需要调用.txt格式的文件  
    with open(os.getcwd() + '/train.txt', 'w') as f:  
        f.write(os.getcwd() + '/train_data.h5\n')  
    print i  
Ejemplo n.º 12
0
def generate(txt, mode, N):
    """
        generate Training Data for LEVEL-2: patches around landmarks
        mode: train or validate
    """
    assert (mode == 'train')
    #从txt文件中获取数据
    data = getDataFromTxt(txt, True,
                          True)  #return [(image_path,landmarks,bbox)]
    #用defaultdict存储数据
    trainData = defaultdict(lambda: dict(landmarks=[], patches=[], label=[]))
    logger(
        "generate 25 positive samples and 500 negative samples for per landmark"
    )

    #产生正负训练样本
    trainData = generateSamples(trainData, data)
    print 'All data:' + str(len(trainData))
    #print trainData['L1']
    #arr = []

    for idx, name in types:  #19个点
        logger('writing training data of %s' % name)
        patches = np.asarray(trainData[name]['patches'])
        landmarks = np.asarray(trainData[name]['landmarks'])
        label = np.asarray(trainData[name]['label'])

        #arr1 = landmarks.reshape(1,-1)
        #arr.append(arr1)

        patches = processImage(patches)
        shuffle_in_unison_scary(patches, landmarks, label)

        #将训练数据保存为hdf5格式
        with h5py.File('train/2_%s/%s.h5' % (name, mode), 'w') as h5:
            h5['data'] = patches.astype(np.float32)
            h5['landmark'] = landmarks.astype(np.float32)
            h5['label'] = label.astype(np.uint8)
        with open('train/2_%s/%s.txt' % (name, mode), 'w') as fd:
            fd.write('train/2_%s/%s.h5' % (name, mode))
        #暂时只产生一个点的数据
        if idx == 1:
            break
Ejemplo n.º 13
0
def generate(ftxt, mode, argument=False):
    """
        Generate Training Data for LEVEL-3
        mode = train or test
    """
    data = getDataFromTxt(ftxt)

    trainData = defaultdict(lambda: dict(patches=[], landmarks=[]))
    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
        assert(img is not None)
        logger("process %s" % imgPath)

        landmarkPs = randomShiftWithArgument(landmarkGt, 0.01)
        if not argument:
            landmarkPs = [landmarkPs[0]]

        for landmarkP in landmarkPs:
            for idx, name, padding in types:
                patch, patch_bbox = getPatch(img, bbox, landmarkP[idx], padding)
                patch = cv2.resize(patch, (15, 15))
                patch = patch.reshape((1, 15, 15))
                trainData[name]['patches'].append(patch)
                _ = patch_bbox.project(bbox.reproject(landmarkGt[idx]))
                trainData[name]['landmarks'].append(_)

    for idx, name, padding in types:
        logger('writing training data of %s'%name)
        patches = np.asarray(trainData[name]['patches'])
        landmarks = np.asarray(trainData[name]['landmarks'])
        patches = processImage(patches)

        shuffle_in_unison_scary(patches, landmarks)

        with h5py.File('train/3_%s/%s.h5'%(name, mode), 'w') as h5:
            h5['data'] = patches.astype(np.float32)
            h5['landmark'] = landmarks.astype(np.float32)
        with open('train/3_%s/%s.txt'%(name, mode), 'w') as fd:
            fd.write('train/3_%s/%s.h5'%(name, mode))
Ejemplo n.º 14
0
def generate_hdf5(ftxt, output, fname, argument=False):

    data = getDataFromTxt(ftxt)
    F_imgs = []
    F_landmarks = []
    EN_imgs = []
    EN_landmarks = []
    NM_imgs = []
    NM_landmarks = []

    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)  #读取灰度图
        assert (img is not None)
        logger("process %s" % imgPath)
        # F
        f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
        f_face = img[f_bbox.top:f_bbox.bottom + 1,
                     f_bbox.left:f_bbox.right + 1]  #截图

        ## data argument
        if argument and np.random.rand() > -1:
            ### flip
            face_flipped, landmark_flipped = flip(
                f_face, landmarkGt)  #图片翻转,数据增强,相应坐标也要发生变换
            face_flipped = cv2.resize(face_flipped, (39, 39))  #固定图片大小
            F_imgs.append(face_flipped.reshape((
                1, 39,
                39)))  #caffe要求输入的格式  ['batch_size','channel','height','width']
            F_landmarks.append(landmark_flipped.reshape(10))  #把10个关键坐标点值变成一维向量
            ### rotation
            # if np.random.rand() > 0.5:
            #     face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox,
            #         bbox.reprojectLandmark(landmarkGt), 5)
            #     landmark_rotated = bbox.projectLandmark(landmark_rotated)
            #     face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
            #     F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
            #     F_landmarks.append(landmark_rotated.reshape(10))
            #     ### flip with rotation
            #     face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
            #     face_flipped = cv2.resize(face_flipped, (39, 39))
            #     F_imgs.append(face_flipped.reshape((1, 39, 39)))
            #     F_landmarks.append(landmark_flipped.reshape(10))
            # ### rotation
            # if np.random.rand() > 0.5:
            #     face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox,
            #         bbox.reprojectLandmark(landmarkGt), -5)
            #     landmark_rotated = bbox.projectLandmark(landmark_rotated)
            #     face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
            #     F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
            #     F_landmarks.append(landmark_rotated.reshape(10))
            #     ### flip with rotation
            #     face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
            #     face_flipped = cv2.resize(face_flipped, (39, 39))
            #     F_imgs.append(face_flipped.reshape((1, 39, 39)))
            #     F_landmarks.append(landmark_flipped.reshape(10))

        f_face = cv2.resize(f_face, (39, 39))
        en_face = f_face[:31, :]
        nm_face = f_face[8:, :]

        f_face = f_face.reshape((1, 39, 39))
        f_landmark = landmarkGt.reshape((10))
        F_imgs.append(f_face)
        F_landmarks.append(f_landmark)

        # EN
        # en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
        # en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(en_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
                (1, 31, 39))
            landmark_flipped = landmark_flipped[:3, :].reshape((6))
            EN_imgs.append(face_flipped)
            EN_landmarks.append(landmark_flipped)

        en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
        en_landmark = landmarkGt[:3, :].reshape((6))
        EN_imgs.append(en_face)
        EN_landmarks.append(en_landmark)

        # NM
        # nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
        # nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
                (1, 31, 39))
            landmark_flipped = landmark_flipped[2:, :].reshape((6))
            NM_imgs.append(face_flipped)
            NM_landmarks.append(landmark_flipped)

        nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
        nm_landmark = landmarkGt[2:, :].reshape((6))
        NM_imgs.append(nm_face)
        NM_landmarks.append(nm_landmark)

    #imgs, landmarks = process_images(ftxt, output)

    F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
    EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
    NM_imgs, NM_landmarks = np.asarray(NM_imgs), np.asarray(NM_landmarks)

    F_imgs = processImage(F_imgs)  #数据标准化
    shuffle_in_unison_scary(F_imgs, F_landmarks)  #随机打乱
    EN_imgs = processImage(EN_imgs)
    shuffle_in_unison_scary(EN_imgs, EN_landmarks)
    NM_imgs = processImage(NM_imgs)
    shuffle_in_unison_scary(NM_imgs, NM_landmarks)

    # full face
    base = join(OUTPUT, '1_F')
    createDir(base)
    output = join(base, fname)  #D:.\deep_landmark\dataset\train\1_F\train.h5
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = F_imgs.astype(np.float32)
        h5['landmark'] = F_landmarks.astype(np.float32)

    # eye and nose
    base = join(OUTPUT, '1_EN')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = EN_imgs.astype(np.float32)
        h5['landmark'] = EN_landmarks.astype(np.float32)

    # nose and mouth
    base = join(OUTPUT, '1_NM')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = NM_imgs.astype(np.float32)
        h5['landmark'] = NM_landmarks.astype(np.float32)
Ejemplo n.º 15
0
def generate_hdf5(ftxt, output, fname, argument=False):

    data = getDataFromTxt(ftxt)
    F_imgs = []
    F_landmarks = []
    EN_imgs = []
    EN_landmarks = []
    NM_imgs = []
    NM_landmarks = []

    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
        assert(img is not None)
        logger("process %s" % imgPath)
        # F
        f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
        f_face = img[f_bbox.top:f_bbox.bottom+1,f_bbox.left:f_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > -1:
            ### flip
            face_flipped, landmark_flipped = flip(f_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (39, 39))
            F_imgs.append(face_flipped.reshape((1, 39, 39)))
            F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), 5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), -5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))

        f_face = cv2.resize(f_face, (39, 39))
        en_face = f_face[:31, :]
        nm_face = f_face[8:, :]

        f_face = f_face.reshape((1, 39, 39))
        f_landmark = landmarkGt.reshape((10))
        F_imgs.append(f_face)
        F_landmarks.append(f_landmark)

        # EN
        # en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
        # en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(en_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
            landmark_flipped = landmark_flipped[:3, :].reshape((6))
            EN_imgs.append(face_flipped)
            EN_landmarks.append(landmark_flipped)

        en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
        en_landmark = landmarkGt[:3, :].reshape((6))
        EN_imgs.append(en_face)
        EN_landmarks.append(en_landmark)

        # NM
        # nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
        # nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
            landmark_flipped = landmark_flipped[2:, :].reshape((6))
            NM_imgs.append(face_flipped)
            NM_landmarks.append(landmark_flipped)

        nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
        nm_landmark = landmarkGt[2:, :].reshape((6))
        NM_imgs.append(nm_face)
        NM_landmarks.append(nm_landmark)

    #imgs, landmarks = process_images(ftxt, output)

    F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
    EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
    NM_imgs, NM_landmarks = np.asarray(NM_imgs),np.asarray(NM_landmarks)

    F_imgs = processImage(F_imgs)
    shuffle_in_unison_scary(F_imgs, F_landmarks)
    EN_imgs = processImage(EN_imgs)
    shuffle_in_unison_scary(EN_imgs, EN_landmarks)
    NM_imgs = processImage(NM_imgs)
    shuffle_in_unison_scary(NM_imgs, NM_landmarks)

    # full face
    base = join(OUTPUT, '1_F')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = F_imgs.astype(np.float32)
        h5['landmark'] = F_landmarks.astype(np.float32)

    # eye and nose
    base = join(OUTPUT, '1_EN')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = EN_imgs.astype(np.float32)
        h5['landmark'] = EN_landmarks.astype(np.float32)

    # nose and mouth
    base = join(OUTPUT, '1_NM')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = NM_imgs.astype(np.float32)
        h5['landmark'] = NM_landmarks.astype(np.float32)
Ejemplo n.º 16
0
def generate_hdf5(ftxt, output, fname, argument=False):
    '''
    生成hdf5
    :param ftxt: 图片路径对应人脸框和左眼睛、右眼睛、鼻子、左嘴角、右嘴角对应坐标
    :param output: hdf5存放位置
    :param fname: 保存名称
    :param argument:
    :return:
    '''
    data = getDataFromTxt(ftxt)
    # 全局图片
    F_imgs = []
    # 全局坐标
    F_landmarks = []
    # 只包含了eye nose的图片
    EN_imgs = []
    # 只包含了eye nose的坐标
    EN_landmarks = []
    # 只包含nose mouth的图片
    NM_imgs = []
    # 只包含了nose mouth的坐标
    NM_landmarks = []

    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)
        assert(img is not None)
        logger("process %s" % imgPath)
        # 人脸框有点小,扩大一点
        f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
        # 人脸框里面的图像
        f_face = img[int(f_bbox.top):int(f_bbox.bottom+1),int(f_bbox.left):int(f_bbox.right+1)]

        ## data argument
        if argument and np.random.rand() > -1:
            ### flip 图片水平翻转,数据增强
            face_flipped, landmark_flipped = flip(f_face, landmarkGt)

            face_flipped = cv2.resize(face_flipped, (39, 39))
            # 1表示通道
            F_imgs.append(face_flipped.reshape((1, 39, 39)))
            # 5*2的关键点转换为一位数组
            F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), 5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), -5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))

        f_face = cv2.resize(f_face, (39, 39))
        # 眼睛和鼻子
        en_face = f_face[:31, :]
        # 鼻子和嘴巴
        nm_face = f_face[8:, :]

        f_face = f_face.reshape((1, 39, 39))
        f_landmark = landmarkGt.reshape((10))
        F_imgs.append(f_face)
        F_landmarks.append(f_landmark)

        # EN
        # en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
        # en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(en_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
            landmark_flipped = landmark_flipped[:3, :].reshape((6))
            EN_imgs.append(face_flipped)
            EN_landmarks.append(landmark_flipped)

        en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
        en_landmark = landmarkGt[:3, :].reshape((6))
        EN_imgs.append(en_face)
        EN_landmarks.append(en_landmark)

        # NM
        # nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
        # nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
            landmark_flipped = landmark_flipped[2:, :].reshape((6))
            NM_imgs.append(face_flipped)
            NM_landmarks.append(landmark_flipped)

        nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
        nm_landmark = landmarkGt[2:, :].reshape((6))
        NM_imgs.append(nm_face)
        NM_landmarks.append(nm_landmark)

    #imgs, landmarks = process_images(ftxt, output)

    F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
    EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
    NM_imgs, NM_landmarks = np.asarray(NM_imgs),np.asarray(NM_landmarks)

    F_imgs = processImage(F_imgs)
    shuffle_in_unison_scary(F_imgs, F_landmarks)
    EN_imgs = processImage(EN_imgs)
    shuffle_in_unison_scary(EN_imgs, EN_landmarks)
    NM_imgs = processImage(NM_imgs)
    shuffle_in_unison_scary(NM_imgs, NM_landmarks)

    # full face
    base = join(OUTPUT, '1_F')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = F_imgs.astype(np.float32)
        h5['landmark'] = F_landmarks.astype(np.float32)

    # eye and nose
    base = join(OUTPUT, '1_EN')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = EN_imgs.astype(np.float32)
        h5['landmark'] = EN_landmarks.astype(np.float32)

    # nose and mouth
    base = join(OUTPUT, '1_NM')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = NM_imgs.astype(np.float32)
        h5['landmark'] = NM_landmarks.astype(np.float32)
Ejemplo n.º 17
0
def generate_hdf5(ftxt, output, fname, argument=False):

    data = getDataFromTxt(ftxt)
    F_imgs = []
    F_landmarks = []
    EN_imgs = []
    EN_landmarks = []
    NM_imgs = []
    NM_landmarks = []

    for (imgPath, bbox, landmarkGt) in data:
        img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
        assert (img is not None)
        logger("process %s" % imgPath)
        # F
        f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
        f_face = img[f_bbox.top:f_bbox.bottom + 1,
                     f_bbox.left:f_bbox.right + 1]

        ## data argument
        if argument and np.random.rand() > -1:
            ### flip
            face_flipped, landmark_flipped = flip(f_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (39, 39))
            F_imgs.append(face_flipped.reshape((1, 39, 39)))
            F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), 5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
                                                   (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
                                                      landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))
            ### rotation
            if np.random.rand() > 0.5:
                face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
                    bbox.reprojectLandmark(landmarkGt), -5)
                landmark_rotated = bbox.projectLandmark(landmark_rotated)
                face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
                                                   (39, 39))
                F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
                F_landmarks.append(landmark_rotated.reshape(10))
                ### flip with rotation
                face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
                                                      landmark_rotated)
                face_flipped = cv2.resize(face_flipped, (39, 39))
                F_imgs.append(face_flipped.reshape((1, 39, 39)))
                F_landmarks.append(landmark_flipped.reshape(10))

        f_face = cv2.resize(f_face, (39, 39))
        en_face = f_face[:31, :]
        nm_face = f_face[8:, :]

        f_face = f_face.reshape((1, 39, 39))
        f_landmark = landmarkGt.reshape((10))
        F_imgs.append(f_face)
        F_landmarks.append(f_landmark)

        # EN
        # en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
        # en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(en_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
                (1, 31, 39))
            landmark_flipped = landmark_flipped[:3, :].reshape((6))
            EN_imgs.append(face_flipped)
            EN_landmarks.append(landmark_flipped)

        en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
        en_landmark = landmarkGt[:3, :].reshape((6))
        EN_imgs.append(en_face)
        EN_landmarks.append(en_landmark)

        # NM
        # nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
        # nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]

        ## data argument
        if argument and np.random.rand() > 0.5:
            ### flip
            face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
            face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
                (1, 31, 39))
            landmark_flipped = landmark_flipped[2:, :].reshape((6))
            NM_imgs.append(face_flipped)
            NM_landmarks.append(landmark_flipped)

        nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
        nm_landmark = landmarkGt[2:, :].reshape((6))
        NM_imgs.append(nm_face)
        NM_landmarks.append(nm_landmark)

    #imgs, landmarks = process_images(ftxt, output)

    F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
    EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
    NM_imgs, NM_landmarks = np.asarray(NM_imgs), np.asarray(NM_landmarks)

    F_imgs = processImage(F_imgs)
    shuffle_in_unison_scary(F_imgs, F_landmarks)
    EN_imgs = processImage(EN_imgs)
    shuffle_in_unison_scary(EN_imgs, EN_landmarks)
    NM_imgs = processImage(NM_imgs)
    shuffle_in_unison_scary(NM_imgs, NM_landmarks)

    # full face
    base = join(OUTPUT, '1_F')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = F_imgs.astype(np.float32)
        h5['landmark'] = F_landmarks.astype(np.float32)

    # eye and nose
    base = join(OUTPUT, '1_EN')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = EN_imgs.astype(np.float32)
        h5['landmark'] = EN_landmarks.astype(np.float32)

    # nose and mouth
    base = join(OUTPUT, '1_NM')
    createDir(base)
    output = join(base, fname)
    logger("generate %s" % output)
    with h5py.File(output, 'w') as h5:
        h5['data'] = NM_imgs.astype(np.float32)
        h5['landmark'] = NM_landmarks.astype(np.float32)