insightface/alignment/coordinate_reg/image_infer.py

import cv2
import numpy as np
import os
import mxnet as mx
from skimage import transform as trans
import insightface


def square_crop(im, S):
    if im.shape[0] > im.shape[1]:
        height = S
        width = int(float(im.shape[1]) / im.shape[0] * S)
        scale = float(S) / im.shape[0]
    else:
        width = S
        height = int(float(im.shape[0]) / im.shape[1] * S)
        scale = float(S) / im.shape[1]
    resized_im = cv2.resize(im, (width, height))
    det_im = np.zeros((S, S, 3), dtype=np.uint8)
    det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
    return det_im, scale


def transform(data, center, output_size, scale, rotation):
    scale_ratio = scale
    rot = float(rotation) * np.pi / 180.0
    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
    t1 = trans.SimilarityTransform(scale=scale_ratio)
    cx = center[0] * scale_ratio
    cy = center[1] * scale_ratio
    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
    t3 = trans.SimilarityTransform(rotation=rot)
    t4 = trans.SimilarityTransform(translation=(output_size / 2,
                                                output_size / 2))
    t = t1 + t2 + t3 + t4
    M = t.params[0:2]
    cropped = cv2.warpAffine(data,
                             M, (output_size, output_size),
                             borderValue=0.0)
    return cropped, M


def trans_points2d(pts, M):
    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
    for i in range(pts.shape[0]):
        pt = pts[i]
        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
        new_pt = np.dot(M, new_pt)
        #print('new_pt', new_pt.shape, new_pt)
        new_pts[i] = new_pt[0:2]

    return new_pts


def trans_points3d(pts, M):
    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
    #print(scale)
    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
    for i in range(pts.shape[0]):
        pt = pts[i]
        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
        new_pt = np.dot(M, new_pt)
        #print('new_pt', new_pt.shape, new_pt)
        new_pts[i][0:2] = new_pt[0:2]
        new_pts[i][2] = pts[i][2] * scale

    return new_pts


def trans_points(pts, M):
    if pts.shape[1] == 2:
        return trans_points2d(pts, M)
    else:
        return trans_points3d(pts, M)


class Handler:
    def __init__(self, prefix, epoch, im_size=192, det_size=224, ctx_id=0):
        print('loading', prefix, epoch)
        if ctx_id >= 0:
            ctx = mx.gpu(ctx_id)
        else:
            ctx = mx.cpu()
        image_size = (im_size, im_size)
        self.detector = insightface.model_zoo.get_model(
            'retinaface_mnet025_v2')  #can replace with your own face detector
        #self.detector = insightface.model_zoo.get_model('retinaface_r50_v1')
        self.detector.prepare(ctx_id=ctx_id)
        self.det_size = det_size
        sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
        all_layers = sym.get_internals()
        sym = all_layers['fc1_output']
        self.image_size = image_size
        model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
        model.bind(for_training=False,
                   data_shapes=[('data', (1, 3, image_size[0], image_size[1]))
                                ])
        model.set_params(arg_params, aux_params)
        self.model = model
        self.image_size = image_size

    def get(self, img, get_all=False):
        out = []
        det_im, det_scale = square_crop(img, self.det_size)
        bboxes, _ = self.detector.detect(det_im)
        if bboxes.shape[0] == 0:
            return out
        bboxes /= det_scale
        if not get_all:
            areas = []
            for i in range(bboxes.shape[0]):
                x = bboxes[i]
                area = (x[2] - x[0]) * (x[3] - x[1])
                areas.append(area)
            m = np.argsort(areas)[-1]
            bboxes = bboxes[m:m + 1]
        for i in range(bboxes.shape[0]):
            bbox = bboxes[i]
            input_blob = np.zeros((1, 3) + self.image_size, dtype=np.float32)
            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.image_size[0] * 2 / 3.0 / max(w, h)
            rimg, M = transform(img, center, self.image_size[0], _scale,
                                rotate)
            rimg = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
            rimg = np.transpose(rimg, (2, 0, 1))  #3*112*112, RGB
            input_blob[0] = rimg
            data = mx.nd.array(input_blob)
            db = mx.io.DataBatch(data=(data, ))
            self.model.forward(db, is_train=False)
            pred = self.model.get_outputs()[-1].asnumpy()[0]
            if pred.shape[0] >= 3000:
                pred = pred.reshape((-1, 3))
            else:
                pred = pred.reshape((-1, 2))
            pred[:, 0:2] += 1
            pred[:, 0:2] *= (self.image_size[0] // 2)
            if pred.shape[1] == 3:
                pred[:, 2] *= (self.image_size[0] // 2)

            IM = cv2.invertAffineTransform(M)
            pred = trans_points(pred, IM)
            out.append(pred)
        return out


if __name__ == '__main__':
    handler = Handler('./model/2d106det', 0, ctx_id=7, det_size=640)
    im = cv2.imread('../../sample-images/t1.jpg')
    tim = im.copy()
    preds = handler.get(im, get_all=True)
    color = (200, 160, 75)
    for pred in preds:
        pred = np.round(pred).astype(np.int)
        for i in range(pred.shape[0]):
            p = tuple(pred[i])
            cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)
    cv2.imwrite('./test_out.jpg', tim)