external/landmark_detection/evaluate.py

import os
import cv2
import math
import argparse
import numpy as np
from tqdm import tqdm

import torch

# private package
from lib import utility



class GetCropMatrix():
    """
    from_shape -> transform_matrix
    """

    def __init__(self, image_size, target_face_scale, align_corners=False):
        self.image_size = image_size
        self.target_face_scale = target_face_scale
        self.align_corners = align_corners

    def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center, to_center):
        cosv = math.cos(angle)
        sinv = math.sin(angle)

        fx, fy = from_center
        tx, ty = to_center

        acos = scale * cosv
        asin = scale * sinv

        a0 = acos
        a1 = -asin
        a2 = tx - acos * fx + asin * fy + shift_xy[0]

        b0 = asin
        b1 = acos
        b2 = ty - asin * fx - acos * fy + shift_xy[1]

        rot_scale_m = np.array([
            [a0, a1, a2],
            [b0, b1, b2],
            [0.0, 0.0, 1.0]
        ], np.float32)
        return rot_scale_m

    def process(self, scale, center_w, center_h):
        if self.align_corners:
            to_w, to_h = self.image_size - 1, self.image_size - 1
        else:
            to_w, to_h = self.image_size, self.image_size

        rot_mu = 0
        scale_mu = self.image_size / (scale * self.target_face_scale * 200.0)
        shift_xy_mu = (0, 0)
        matrix = self._compose_rotate_and_scale(
            rot_mu, scale_mu, shift_xy_mu,
            from_center=[center_w, center_h],
            to_center=[to_w / 2.0, to_h / 2.0])
        return matrix


class TransformPerspective():
    """
    image, matrix3x3 -> transformed_image
    """

    def __init__(self, image_size):
        self.image_size = image_size

    def process(self, image, matrix):
        return cv2.warpPerspective(
            image, matrix, dsize=(self.image_size, self.image_size),
            flags=cv2.INTER_LINEAR, borderValue=0)


class TransformPoints2D():
    """
    points (nx2), matrix (3x3) -> points (nx2)
    """

    def process(self, srcPoints, matrix):
        # nx3
        desPoints = np.concatenate([srcPoints, np.ones_like(srcPoints[:, [0]])], axis=1)
        desPoints = desPoints @ np.transpose(matrix)  # nx3
        desPoints = desPoints[:, :2] / desPoints[:, [2, 2]]
        return desPoints.astype(srcPoints.dtype)


class Alignment:
    def __init__(self, args, model_path, dl_framework, device_ids):
        self.input_size = 256
        self.target_face_scale = 1.0
        self.dl_framework = dl_framework

        # model
        if self.dl_framework == "pytorch":
            # conf
            self.config = utility.get_config(args)
            self.config.device_id = device_ids[0]
            # set environment
            utility.set_environment(self.config)
            self.config.init_instance()
            if self.config.logger is not None:
                self.config.logger.info("Loaded configure file %s: %s" % (args.config_name, self.config.id))
                self.config.logger.info("\n" + "\n".join(["%s: %s" % item for item in self.config.__dict__.items()]))

            net = utility.get_net(self.config)
            if device_ids == [-1]:
                checkpoint = torch.load(model_path, map_location="cpu")
            else:
                checkpoint = torch.load(model_path)
            net.load_state_dict(checkpoint["net"])
            net = net.to(self.config.device_id)
            net.eval()
            self.alignment = net
        else:
            assert False

        self.getCropMatrix = GetCropMatrix(image_size=self.input_size, target_face_scale=self.target_face_scale,
                                           align_corners=True)
        self.transformPerspective = TransformPerspective(image_size=self.input_size)
        self.transformPoints2D = TransformPoints2D()

    def norm_points(self, points, align_corners=False):
        if align_corners:
            # [0, SIZE-1] -> [-1, +1]
            return points / torch.tensor([self.input_size - 1, self.input_size - 1]).to(points).view(1, 1, 2) * 2 - 1
        else:
            # [-0.5, SIZE-0.5] -> [-1, +1]
            return (points * 2 + 1) / torch.tensor([self.input_size, self.input_size]).to(points).view(1, 1, 2) - 1

    def denorm_points(self, points, align_corners=False):
        if align_corners:
            # [-1, +1] -> [0, SIZE-1]
            return (points + 1) / 2 * torch.tensor([self.input_size - 1, self.input_size - 1]).to(points).view(1, 1, 2)
        else:
            # [-1, +1] -> [-0.5, SIZE-0.5]
            return ((points + 1) * torch.tensor([self.input_size, self.input_size]).to(points).view(1, 1, 2) - 1) / 2

    def preprocess(self, image, scale, center_w, center_h):
        matrix = self.getCropMatrix.process(scale, center_w, center_h)
        input_tensor = self.transformPerspective.process(image, matrix)
        input_tensor = input_tensor[np.newaxis, :]

        input_tensor = torch.from_numpy(input_tensor)
        input_tensor = input_tensor.float().permute(0, 3, 1, 2)
        input_tensor = input_tensor / 255.0 * 2.0 - 1.0
        input_tensor = input_tensor.to(self.config.device_id)
        return input_tensor, matrix

    def postprocess(self, srcPoints, coeff):
        # dstPoints = self.transformPoints2D.process(srcPoints, coeff)
        # matrix^(-1) * src = dst
        # src = matrix * dst
        dstPoints = np.zeros(srcPoints.shape, dtype=np.float32)
        for i in range(srcPoints.shape[0]):
            dstPoints[i][0] = coeff[0][0] * srcPoints[i][0] + coeff[0][1] * srcPoints[i][1] + coeff[0][2]
            dstPoints[i][1] = coeff[1][0] * srcPoints[i][0] + coeff[1][1] * srcPoints[i][1] + coeff[1][2]
        return dstPoints

    def analyze(self, image, scale, center_w, center_h):
        input_tensor, matrix = self.preprocess(image, scale, center_w, center_h)

        if self.dl_framework == "pytorch":
            with torch.no_grad():
                output = self.alignment(input_tensor)
            landmarks = output[-1][0]
        else:
            assert False

        landmarks = self.denorm_points(landmarks)
        landmarks = landmarks.data.cpu().numpy()[0]
        landmarks = self.postprocess(landmarks, np.linalg.inv(matrix))

        return landmarks


def L2(p1, p2):
    return np.linalg.norm(p1 - p2)


def NME(landmarks_gt, landmarks_pv):
    pts_num = landmarks_gt.shape[0]
    if pts_num == 29:
        left_index = 16
        right_index = 17
    elif pts_num == 68:
        left_index = 36
        right_index = 45
    elif pts_num == 98:
        left_index = 60
        right_index = 72

    nme = 0
    eye_span = L2(landmarks_gt[left_index], landmarks_gt[right_index])
    for i in range(pts_num):
        error = L2(landmarks_pv[i], landmarks_gt[i])
        nme += error / eye_span
    nme /= pts_num
    return nme


def evaluate(args, model_path, metadata_path, device_ids, mode):
    alignment = Alignment(args, model_path, dl_framework="pytorch", device_ids=device_ids)
    config = alignment.config
    nme_sum = 0
    with open(metadata_path, 'r') as f:
        lines = f.readlines()
    for k, line in enumerate(tqdm(lines)):
        item = line.strip().split("\t")
        image_name, landmarks_5pts, landmarks_gt, scale, center_w, center_h = item[:6]
        # image & keypoints alignment
        image_name = image_name.replace('\\', '/')
        image_name = image_name.replace('//msr-facestore/Workspace/MSRA_EP_Allergan/users/yanghuan/training_data/wflw/rawImages/', '')
        image_name = image_name.replace('./rawImages/', '')
        image_path = os.path.join(config.image_dir, image_name)
        landmarks_gt = np.array(list(map(float, landmarks_gt.split(","))), dtype=np.float32).reshape(-1, 2)
        scale, center_w, center_h = float(scale), float(center_w), float(center_h)

        image = cv2.imread(image_path)
        landmarks_pv = alignment.analyze(image, scale, center_w, center_h)

        # NME
        if mode == "nme":
            nme = NME(landmarks_gt, landmarks_pv)
            nme_sum += nme
            # print("Current NME(%d): %f" % (k + 1, (nme_sum / (k + 1))))
        else:
            pass

    if mode == "nme":
        print("Final NME: %f" % (100*nme_sum / (k + 1)))
    else:
        pass


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Evaluation script")
    parser.add_argument("--config_name", type=str, default="alignment", help="set configure file name")
    parser.add_argument("--model_path", type=str, default="./train.pkl", help="the path of model")
    parser.add_argument("--data_definition", type=str, default='WFLW', help="COFW/300W/WFLW")
    parser.add_argument("--metadata_path", type=str, default="", help="the path of metadata")
    parser.add_argument("--image_dir", type=str, default="", help="the path of image")
    parser.add_argument("--device_ids", type=str, default="0", help="set device ids, -1 means use cpu device, >= 0 means use gpu device")
    parser.add_argument("--mode", type=str, default="nme", help="set the evaluate mode: nme")
    args = parser.parse_args()

    device_ids = list(map(int, args.device_ids.split(",")))
    evaluate(
        args,
        model_path=args.model_path,
        metadata_path=args.metadata_path,
        device_ids=device_ids,
        mode=args.mode)