def GenerateData(ftxt, data_path, output, net, argument=False):
    if net == "PNet":
        size = 12
    elif net == "RNet":
        size = 24
    elif net == "ONet":
        size = 48
    else:
        print('Net type error')
        return
    image_id = 0
    f = open(join(OUTPUT, "landmark_%s.txt" % (size)), 'w')
    data = getDataFromTxt(ftxt, data_path)
    idx = 0
    #image_path bbox landmark(5*2)
    for (imgPath, bbox, landmarkGt) in data:
        #print imgPath
        F_imgs = []
        F_landmarks = []
        img = cv2.imread(imgPath)
        assert (img is not None)
        img_h, img_w, img_c = img.shape
        gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
        f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
        f_face = cv2.resize(f_face, (size, size))
        landmark = np.zeros((5, 2))
        #normalize
        for index, one in enumerate(landmarkGt):
            rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
                  (one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
            landmark[index] = rv

        F_imgs.append(f_face)
        F_landmarks.append(landmark.reshape(10))
        landmark = np.zeros((5, 2))
        if argument:
            idx = idx + 1
            if idx % 100 == 0:
                print(idx, "images done")
            x1, y1, x2, y2 = gt_box
            #gt's width
            gt_w = x2 - x1 + 1
            #gt's height
            gt_h = y2 - y1 + 1
            if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
                continue
            #random shift
            for i in range(10):
                bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
                                        np.ceil(1.25 * max(gt_w, gt_h)))
                delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
                delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
                nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
                ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))

                nx2 = nx1 + bbox_size
                ny2 = ny1 + bbox_size
                if nx2 > img_w or ny2 > img_h:
                    continue
                crop_box = np.array([nx1, ny1, nx2, ny2])
                cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
                resized_im = cv2.resize(cropped_im, (size, size))
                #cal iou
                iou = IoU(crop_box, np.expand_dims(gt_box, 0))
                if iou > 0.65:
                    F_imgs.append(resized_im)
                    #normalize
                    for index, one in enumerate(landmarkGt):
                        rv = ((one[0] - nx1) / bbox_size,
                              (one[1] - ny1) / bbox_size)
                        landmark[index] = rv
                    F_landmarks.append(landmark.reshape(10))
                    landmark = np.zeros((5, 2))
                    landmark_ = F_landmarks[-1].reshape(-1, 2)
                    bbox = BBox([nx1, ny1, nx2, ny2])

                    #mirror
                    if random.choice([0, 1]) > 0:
                        face_flipped, landmark_flipped = flip(
                            resized_im, landmark_)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        #c*h*w
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))
                    #rotate
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
                        #landmark_offset
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        #flip
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

                    #inverse clockwise rotation
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

            F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
            #print F_imgs.shape
            #print F_landmarks.shape
            for i in range(len(F_imgs)):
                #print(image_id)

                if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
                    continue

                if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
                    continue

                cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
                landmarks = map(str, list(F_landmarks[i]))
                f.write(
                    join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
                    " ".join(landmarks) + "\n")
                image_id = image_id + 1

    #print F_imgs.shape
    #print F_landmarks.shape
    #F_imgs = processImage(F_imgs)
    #shuffle_in_unison_scary(F_imgs, F_landmarks)

    f.close()
    return F_imgs, F_landmarks
Exemplo n.º 2
0
def GenerateData(ftxt, data_path, net, argument=False):
    '''

    :param ftxt: name/path of the text file that contains image path,
                bounding box, and landmarks

    :param output: path of the output dir
    :param net: one of the net in the cascaded networks
    :param argument: apply augmentation or not
    :return:  images and related landmarks
    '''
    if net == "PNet":
        size = 12
    elif net == "RNet":
        size = 24
    elif net == "ONet":
        size = 48
    else:
        print('Net type error')
        return
    image_id = 0
    #
    f = open(join(OUTPUT, "landmark_%s_aug.txt" % (size)), 'w')
    #dstdir = "train_landmark_few"
    # get image path , bounding box, and landmarks from file 'ftxt'
    data = getDataFromTxt(ftxt, data_path=data_path)  # 图片路径,框-4,标注-(5,2)
    idx = 0
    #image_path bbox landmark(5*2)
    for (imgPath, bbox, landmarkGt) in data:
        #print imgPath
        F_imgs = []
        F_landmarks = []
        #print(imgPath)
        img = cv2.imread(imgPath)

        assert (img is not None)
        img_h, img_w, img_c = img.shape
        gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
        #get sub-image from bbox        得到框出来的图
        f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
        # resize the gt image to specified size     将大小调整指定的尺寸
        f_face = cv2.resize(f_face, (size, size))
        #initialize the landmark
        landmark = np.zeros((5, 2))

        #normalize land mark by dividing the width and height of the ground truth bounding box
        # landmakrGt is a list of tuples    对标注进行归一化(除以框)
        for index, one in enumerate(landmarkGt):
            # (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
            rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
                  (one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
            # put the normalized value into the new list landmark
            landmark[index] = rv

        F_imgs.append(f_face)
        F_landmarks.append(landmark.reshape(10))
        landmark = np.zeros((5, 2))
        if argument:  # 数据集扩展
            idx = idx + 1
            if idx % 100 == 0:
                print(idx, "images done")
            x1, y1, x2, y2 = gt_box
            #gt's width
            gt_w = x2 - x1 + 1
            #gt's height
            gt_h = y2 - y1 + 1
            if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:  # 框的大小限制
                continue
            #random shift
            for i in range(10):
                bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
                                        np.ceil(1.25 *
                                                max(gt_w, gt_h)))  # 框的大小
                delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
                delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
                nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
                ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))

                nx2 = nx1 + bbox_size
                ny2 = ny1 + bbox_size
                if nx2 > img_w or ny2 > img_h:
                    continue
                crop_box = np.array([nx1, ny1, nx2, ny2])

                cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
                resized_im = cv2.resize(cropped_im, (size, size))
                #cal iou
                iou = IoU(crop_box, np.expand_dims(gt_box, 0))
                if iou > 0.65:
                    F_imgs.append(resized_im)
                    #normalize
                    for index, one in enumerate(landmarkGt):
                        rv = ((one[0] - nx1) / bbox_size,
                              (one[1] - ny1) / bbox_size)
                        landmark[index] = rv
                    F_landmarks.append(landmark.reshape(10))
                    landmark = np.zeros((5, 2))
                    landmark_ = F_landmarks[-1].reshape(-1, 2)
                    bbox = BBox([nx1, ny1, nx2, ny2])

                    #mirror
                    if random.choice([0, 1]) > 0:
                        face_flipped, landmark_flipped = flip(
                            resized_im, landmark_)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        #c*h*w
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))
                    #rotate
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
                        #landmark_offset
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        #flip
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

                    #anti-clockwise rotation
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

            F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
            #print F_imgs.shape
            #print F_landmarks.shape
            for i in range(len(F_imgs)):
                #if image_id % 100 == 0:

                #print('image id : ', image_id)

                if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
                    continue

                if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
                    continue

                cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
                landmarks = map(str, list(F_landmarks[i]))
                f.write(
                    join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
                    " ".join(landmarks) + "\n")
                image_id = image_id + 1

    #print F_imgs.shape
    #print F_landmarks.shape
    #F_imgs = processImage(F_imgs)
    #shuffle_in_unison_scary(F_imgs, F_landmarks)

    f.close()
    return F_imgs, F_landmarks
Exemplo n.º 3
0
def generateData_aug(data_dir, net, argument=False):

    if net == "PNet":
        size = 12
    elif net == "RNet":
        size = 24
    elif net == "ONet":
        size = 48
    else:
        print("Net type error! ")
        return

    OUTPUT = data_dir + "/%d" % size
    if not exists(OUTPUT): os.mkdir(OUTPUT)
    dstdir = data_dir + "/%d/train_%s_landmark_aug" % (size, net)
    if not exists(dstdir): os.mkdir(dstdir)
    assert (exists(dstdir) and exists(OUTPUT))

    # get image path , bounding box, and landmarks from file 'ftxt'
    data = getDataFromTxt("./prepare_data/trainImageList.txt",
                          data_path=data_dir + '/Align')
    f = open(join(OUTPUT, "landmark_%s_aug.txt" % (size)), 'w')

    image_id = 0
    idx = 0
    for (imgPath, bbox, landmarkGt) in data:
        F_imgs = []
        F_landmarks = []
        img = cv2.imread(imgPath)

        assert (img is not None)
        img_h, img_w, img_c = img.shape
        gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
        # get sub-image from bbox
        f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
        # resize the gt image to specified size
        f_face = cv2.resize(f_face, (size, size))
        # initialize the landmark
        landmark = np.zeros((5, 2))

        # normalize land mark by dividing the width and height of the ground truth bounding box
        # landmakrGt is a list of tuples
        for index, one in enumerate(landmarkGt):
            # (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
            rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
                  (one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
            landmark[index] = rv

        F_imgs.append(f_face)
        F_landmarks.append(landmark.reshape(10))

        landmark = np.zeros((5, 2))
        if argument:
            idx = idx + 1
            if idx % 100 == 0:
                print(idx, "images done")
            x1, y1, x2, y2 = gt_box
            gt_w = x2 - x1 + 1
            gt_h = y2 - y1 + 1
            if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
                continue
            #random shift
            for i in range(10):
                bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
                                        np.ceil(1.25 * max(gt_w, gt_h)))
                delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
                delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
                nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
                ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))

                nx2 = nx1 + bbox_size
                ny2 = ny1 + bbox_size
                if nx2 > img_w or ny2 > img_h:
                    continue
                crop_box = np.array([nx1, ny1, nx2, ny2])

                cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
                resized_im = cv2.resize(cropped_im, (size, size))
                #cal iou
                iou = IoU(crop_box, np.expand_dims(gt_box, 0))
                if iou > 0.65:
                    F_imgs.append(resized_im)
                    #normalize
                    for index, one in enumerate(landmarkGt):
                        rv = ((one[0] - nx1) / bbox_size,
                              (one[1] - ny1) / bbox_size)
                        landmark[index] = rv
                    F_landmarks.append(landmark.reshape(10))
                    landmark = np.zeros((5, 2))
                    landmark_ = F_landmarks[-1].reshape(-1, 2)
                    bbox = BBox([nx1, ny1, nx2, ny2])
                    #mirror
                    if random.choice([0, 1]) > 0:
                        face_flipped, landmark_flipped = flip(
                            resized_im, landmark_)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        #c*h*w
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))
                    #rotate
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
                        #landmark_offset
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        #flip
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))
                    #anti-clockwise rotation
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

            F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)

            for i in range(len(F_imgs)):

                if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
                    continue
                if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
                    continue

                cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
                landmarks = map(str, list(F_landmarks[i]))
                f.write(dstdir + "/%d.jpg" % (image_id) + " -2 " +
                        " ".join(landmarks) + "\n")
                image_id = image_id + 1

    f.close()
    return F_imgs, F_landmarks
def GenerateData(ftxt,
                 data_path,
                 output_path,
                 img_output_path,
                 net,
                 argument=False):
    '''
    参数
    ------------

        ftxt: path of anno file
        data_path: 数据集所在目录
        output_path: 文本文件输出目录地址
        img_output_path: 图片输出地址
        net: String 三个网络之一的名字
        argument: 是否使用数据增强
    
    返回值
    -------------
        images and related landmarks
    '''
    if net == "PNet":
        size = 12
    elif net == "RNet":
        size = 24
    elif net == "ONet":
        size = 48
    else:
        print('Net type error')
        return
    image_id = 0
    #
    f = open(join(output_path, "landmark_%s_aug.txt" % (size)), 'w')
    #img_output_path = "train_landmark_few"
    # get image path , bounding box, and landmarks from file 'ftxt'
    data = getDataFromTxt(ftxt, data_path=data_path)
    idx = 0
    #image_path bbox landmark(5*2)
    for (imgPath, bbox, landmarkGt) in data:
        #print imgPath
        F_imgs = []
        F_landmarks = []
        #print(imgPath)
        img = cv2.imread(imgPath)

        assert (img is not None)
        img_h, img_w, img_c = img.shape
        gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
        #get sub-image from bbox
        f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
        # resize the gt image to specified size
        f_face = cv2.resize(f_face, (size, size))
        #initialize the landmark
        landmark = np.zeros((5, 2))

        #normalize land mark by dividing the width and height of the ground truth bounding box
        # landmakrGt is a list of tuples
        for index, one in enumerate(landmarkGt):
            # 重新计算因裁剪过后而改变的landmark的坐标,并且进行归一化
            # (x - bbox.left) / width of bbox, (y - bbox.top) / height of bbox
            rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
                  (one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
            landmark[index] = rv

        F_imgs.append(f_face)
        F_landmarks.append(landmark.reshape(10))  #[x1, y1, x2, y2, ...]
        landmark = np.zeros((5, 2))
        # data augment
        if argument:
            idx = idx + 1
            if idx % 100 == 0:
                print(idx, "images done")
            x1, y1, x2, y2 = gt_box
            #gt's width
            gt_w = x2 - x1 + 1
            #gt's height
            gt_h = y2 - y1 + 1
            if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
                continue
            #random shift
            for i in range(10):
                bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
                                        np.ceil(1.25 * max(gt_w, gt_h)))
                delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
                delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
                nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
                ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))

                nx2 = nx1 + bbox_size
                ny2 = ny1 + bbox_size
                if nx2 > img_w or ny2 > img_h:
                    continue
                crop_box = np.array([nx1, ny1, nx2, ny2])

                cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
                resized_im = cv2.resize(cropped_im, (size, size))
                #calculate iou
                iou = IoU(crop_box, np.expand_dims(gt_box, 0))
                if iou > 0.65:
                    F_imgs.append(resized_im)
                    #normalize
                    for index, one in enumerate(landmarkGt):
                        rv = ((one[0] - nx1) / bbox_size,
                              (one[1] - ny1) / bbox_size)
                        landmark[index] = rv
                    F_landmarks.append(landmark.reshape(10))
                    landmark = np.zeros((5, 2))
                    landmark_ = F_landmarks[-1].reshape(-1, 2)
                    bbox = BBox([nx1, ny1, nx2, ny2])

                    #mirror
                    if random.choice([0, 1]) > 0:
                        face_flipped, landmark_flipped = flip(
                            resized_im, landmark_)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        #c*h*w
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))
                    #rotate
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
                        #landmark_offset
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        #flip
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

                    #anti-clockwise rotation
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
                                                                         bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        F_imgs.append(face_rotated_by_alpha)
                        F_landmarks.append(landmark_rotated.reshape(10))

                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        F_imgs.append(face_flipped)
                        F_landmarks.append(landmark_flipped.reshape(10))

            F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
            #print F_imgs.shape
            #print F_landmarks.shape
            for i in range(len(F_imgs)):
                # 只要有一个坐标小于0或大于1就舍弃
                if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
                    continue
                if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
                    continue

                cv2.imwrite(join(img_output_path, "%d.jpg" % (image_id)),
                            F_imgs[i])
                landmarks = map(str, list(F_landmarks[i]))
                f.write(
                    join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
                    " ".join(landmarks) + "\n")
                image_id = image_id + 1

    #print F_imgs.shape
    #print F_landmarks.shape
    #F_imgs = processImage(F_imgs)
    #shuffle_in_unison_scary(F_imgs, F_landmarks)

    f.close()
    return F_imgs, F_landmarks
Exemplo n.º 5
0
def generate_landmark_data(landmark_truth_txt_path,
                           images_dir,
                           net,
                           argument=False):
    """ 为特定网络类型生成关键点训练样本,label=-2
    :param landmark_truth_txt_path: 包含image path, bounding box, and landmarks的txt路径
    :param images_dir: 图片文件夹路径
    :param net: 网络类型,('PNet', 'RNet', 'ONet')
    :param argument: 是否进行数据增强
    :return:  images and related landmarks
    """
    if net == "PNet":
        size = 12
        landmark_dir = path_config.pnet_landmark_dir
        net_data_root_dir = path_config.pnet_dir
        landmark_file = open(path_config.pnet_landmark_txt_path, 'w')
    elif net == "RNet":
        size = 24
        landmark_dir = path_config.rnet_landmark_dir
        net_data_root_dir = path_config.rnet_dir
        landmark_file = open(path_config.rnet_landmark_txt_path, 'w')
    elif net == "ONet":
        size = 48
        landmark_dir = path_config.onet_landmark_dir
        net_data_root_dir = path_config.onet_dir
        landmark_file = open(path_config.onet_landmark_txt_path, 'w')
    else:
        raise ValueError('网络类型(--net)错误!')

    if not os.path.exists(net_data_root_dir):
        os.mkdir(net_data_root_dir)
    if not os.path.exists(landmark_dir):
        os.mkdir(landmark_dir)

    # 读取关键点信息文件:image path , bounding box, and landmarks
    data = get_landmark_data(landmark_truth_txt_path, images_dir)
    # 针对每张图片,生成关键点训练数据
    landmark_idx = 0
    image_id = 0
    for (imgPath, bbox, landmarkGt) in data:
        # 截取的图片数据和图片中关键点位置数据
        cropped_images = []
        cropped_landmarks = []

        img = cv2.imread(imgPath)
        assert (img is not None)
        image_height, image_width, _ = img.shape

        gt_box = np.array([[bbox.left, bbox.top, bbox.right, bbox.bottom]])
        square_gt_box = np.squeeze(convert_to_square(gt_box))
        # 防止越界,同时保持方形
        if square_gt_box[0] < 0:
            square_gt_box[2] -= square_gt_box[0]
            square_gt_box[0] = 0
        if square_gt_box[1] < 0:
            square_gt_box[3] -= square_gt_box[1]
            square_gt_box[1] = 0
        if square_gt_box[2] > image_width:
            square_gt_box[0] -= (square_gt_box[2] - image_width)
            square_gt_box[2] = image_width
        if square_gt_box[3] > image_height:
            square_gt_box[1] -= (square_gt_box[3] - image_height)
            square_gt_box[3] = image_height

        gt_box = np.squeeze(gt_box)
        # 计算标准化的关键点坐标
        landmark = np.zeros((5, 2))
        for index, one in enumerate(landmarkGt):
            # (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
            landmark[index] = ((one[0] - square_gt_box[0]) /
                               (square_gt_box[2] - square_gt_box[0]),
                               (one[1] - square_gt_box[1]) /
                               (square_gt_box[3] - square_gt_box[1]))
        cropped_landmarks.append(landmark.reshape(10))

        # 截取目标区域图片
        cropped_object_image = img[square_gt_box[1]:square_gt_box[3] + 1,
                                   square_gt_box[0]:square_gt_box[2] + 1]
        cropped_object_image = cv2.resize(cropped_object_image, (size, size))
        cropped_images.append(cropped_object_image)

        landmark = np.zeros((5, 2))
        if argument:
            landmark_idx = landmark_idx + 1
            if landmark_idx % 100 == 0:
                sys.stdout.write("\r{}/{} images done ...".format(
                    landmark_idx, len(data)))

            # ground truth的坐标、宽和高
            x_truth_left, y_truth_top, x_truth_right, y_truth_bottom = gt_box
            width_truth = x_truth_right - x_truth_left + 1
            height_truth = y_truth_bottom - y_truth_top + 1
            if max(width_truth,
                   height_truth) < 20 or x_truth_left < 0 or y_truth_top < 0:
                continue
            # 随机偏移
            shift_num = 0
            shift_try = 0
            while shift_num < 10 and shift_try < 100:
                bbox_size = npr.randint(
                    int(min(width_truth, height_truth) * 0.8),
                    np.ceil(1.25 * max(width_truth, height_truth)))
                delta_x = npr.randint(int(-width_truth * 0.2),
                                      np.ceil(width_truth * 0.2))
                delta_y = npr.randint(int(-height_truth * 0.2),
                                      np.ceil(height_truth * 0.2))
                x_left_shift = int(
                    max(
                        x_truth_left + width_truth / 2 - bbox_size / 2 +
                        delta_x, 0))
                y_top_shift = int(
                    max(
                        y_truth_top + height_truth / 2 - bbox_size / 2 +
                        delta_y, 0))
                x_right_shift = x_left_shift + bbox_size
                y_bottom_shift = y_top_shift + bbox_size
                if x_right_shift > image_width or y_bottom_shift > image_height:
                    shift_try += 1
                    continue
                crop_box = np.array(
                    [x_left_shift, y_top_shift, x_right_shift, y_bottom_shift])
                # 计算数据增强后的偏移区域和ground truth的方形校正IoU
                iou = square_IoU(crop_box, np.expand_dims(gt_box, 0))
                if iou > 0.65:
                    shift_num += 1
                    cropped_im = img[y_top_shift:y_bottom_shift + 1,
                                     x_left_shift:x_right_shift + 1, :]
                    resized_im = cv2.resize(cropped_im, (size, size))
                    cropped_images.append(resized_im)
                    # 标准化
                    for index, one in enumerate(landmarkGt):
                        landmark[index] = ((one[0] - x_left_shift) / bbox_size,
                                           (one[1] - y_top_shift) / bbox_size)
                    cropped_landmarks.append(landmark.reshape(10))

                    # 进行其他类型的数据增强
                    landmark = np.zeros((5, 2))
                    landmark_ = cropped_landmarks[-1].reshape(-1, 2)
                    bbox = BBox([
                        x_left_shift, y_top_shift, x_right_shift,
                        y_bottom_shift
                    ])
                    # 镜像
                    if random.choice([0, 1]) > 0:
                        face_flipped, landmark_flipped = flip(
                            resized_im, landmark_)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        cropped_images.append(face_flipped)
                        cropped_landmarks.append(landmark_flipped.reshape(10))

                    # 顺时针旋转
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = \
                            rotate(img, bbox, bbox.reprojectLandmark(landmark_), 5)
                        # landmark_offset
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        cropped_images.append(face_rotated_by_alpha)
                        cropped_landmarks.append(landmark_rotated.reshape(10))
                        # 上下翻转
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        cropped_images.append(face_flipped)
                        cropped_landmarks.append(landmark_flipped.reshape(10))

                    # 逆时针旋转
                    if random.choice([0, 1]) > 0:
                        face_rotated_by_alpha, landmark_rotated = \
                            rotate(img, bbox, bbox.reprojectLandmark(landmark_), -5)
                        landmark_rotated = bbox.projectLandmark(
                            landmark_rotated)
                        face_rotated_by_alpha = cv2.resize(
                            face_rotated_by_alpha, (size, size))
                        cropped_images.append(face_rotated_by_alpha)
                        cropped_landmarks.append(landmark_rotated.reshape(10))
                        # 上下翻转
                        face_flipped, landmark_flipped = flip(
                            face_rotated_by_alpha, landmark_rotated)
                        face_flipped = cv2.resize(face_flipped, (size, size))
                        cropped_images.append(face_flipped)
                        cropped_landmarks.append(landmark_flipped.reshape(10))
                else:
                    shift_try += 1

        # 保存关键点训练图片及坐标信息
        cropped_images, cropped_landmarks = np.asarray(
            cropped_images), np.asarray(cropped_landmarks)
        for i in range(len(cropped_images)):
            if np.any(cropped_landmarks[i] < 0):
                continue
            if np.any(cropped_landmarks[i] > 1):
                continue

            cv2.imwrite(os.path.join(landmark_dir, "%d.jpg" % image_id),
                        cropped_images[i])
            landmarks = map(str, list(cropped_landmarks[i]))
            landmark_file.write(
                os.path.join(landmark_dir, "%d.jpg" % image_id) + " -2 " +
                " ".join(landmarks) + "\n")
            image_id = image_id + 1

    landmark_file.close()
    return