main_gen_new_puppet.py

import sys
from facewarp.gen_puppet_utils import *

''' ================================================
            FOA face landmark detection 
================================================ '''

data_dir = out_dir = 'examples_cartoon'
test_data = sys.argv[1] # for example 'roy_example.png'
CH = test_data[:-4]
use_gt_bb = False

if(not os.path.exists(os.path.join(data_dir, CH + '.pts'))):

    from thirdparty.face_of_art.menpo_functions import *
    from thirdparty.face_of_art.deep_heatmaps_model_fusion_net import DeepHeatmapsModel

    model_path = 'examples/ckpt/deep_heatmaps-60000'  # model for estimation stage
    pdm_path = 'thirdparty/face_of_art/pdm_clm_models/pdm_models/'  # models for correction stage
    clm_path = 'thirdparty/face_of_art/pdm_clm_models/clm_models/g_t_all'  # model for tuning stage

    outline_tune = True  # if true use tuning stage on eyebrows+jaw, else use tuning stage on jaw only
    map_landmarks_to_original_image = True  # if True, landmark predictions will be mapped to match original
    # input image size. otherwise the predicted landmarks will match the cropped version (256x256) of the images

    # load images
    bb_dir = os.path.join(data_dir, 'Bounding_Boxes')
    bb_dictionary = load_bb_dictionary(bb_dir, mode='TEST', test_data=test_data)
    bb_type = 'init'

    img_list = load_menpo_image_list(
        img_dir=data_dir, test_data=test_data, train_crop_dir=data_dir, img_dir_ns=data_dir, bb_type=bb_type,
        bb_dictionary=bb_dictionary, mode='TEST', return_transform=map_landmarks_to_original_image)

    # load model
    heatmap_model = DeepHeatmapsModel(
        mode='TEST', img_path=data_dir, test_model_path=model_path, test_data=test_data, menpo_verbose=False)

    print ("\npredicting landmarks for: "+os.path.join(data_dir, test_data))
    print ("\nsaving landmarks to: "+out_dir)
    for i, img in enumerate(img_list):
        if i == 0:
            reuse = None
        else:
            reuse = True

        preds = heatmap_model.get_landmark_predictions(img_list=[img], pdm_models_dir=pdm_path, clm_model_path=clm_path,
                                                       reuse=reuse, map_to_input_size=map_landmarks_to_original_image)

        if map_landmarks_to_original_image:
            img = img[0]

        if outline_tune:
            pred_lms = preds['ECpTp_out']
        else:
            pred_lms = preds['ECpTp_jaw']

        mio.export_landmark_file(PointCloud(pred_lms[0]), os.path.join(out_dir, img.path.stem + '.pts'),
                                 overwrite=True)

    print ("\nFOA landmark detection DONE!")


''' ====================================================================
        opencv vis and refine landmark 
        
1. visualize the automatic detection result from FOA approach   
2. click on landmarks and move them if they are not correct 

Press Q to save landmarks and continue.
==================================================================== '''

import cv2
import numpy as np
import os

if(os.path.exists(os.path.join(data_dir, CH + '_face_open_mouth.txt'))):
    pts0 = np.loadtxt(os.path.join(data_dir, CH + '_face_open_mouth.txt'))
    pts0 = pts0[:, 0:2]
else:
    f = open(os.path.join(data_dir, test_data[:-4] + '.pts'), 'r')
    lines = f.readlines()
    pts = []
    for i in range(3, 3+68):
        line = lines[i]
        line = line[:-1].split(' ')
        pts += [float(item) for item in line]
    pts0 = np.array(pts).reshape((68, 2))

pts = np.copy(pts0)
img0 = cv2.imread(os.path.join(data_dir, test_data))
img = np.copy(img0)
node = -1


def click_adjust_wireframe(event, x, y, flags, param):
    global img, pts, node

    def update_img(node, button_up=False):
        global img, pts

        # update carton points object and get fresh pts list
        pts[node, 0], pts[node, 1] = x, y

        img = np.copy(img0)
        draw_landmarks(img, pts)

        # zoom-in feature
        if (not button_up):
            zoom_in_scale = 2
            zoom_in_box_size = int(150 / zoom_in_scale)
            zoom_in_range = int(np.min([zoom_in_box_size, x, y,
                                        (img.shape[0] - y) / 2 / zoom_in_scale,
                                        (img.shape[1] - x) / 2 / zoom_in_scale]))

            img_zoom_in = img[y - zoom_in_range:y + zoom_in_range,
                          x - zoom_in_range:x + zoom_in_range].copy()
            img_zoom_in = cv2.resize(img_zoom_in, (0, 0), fx=zoom_in_scale,
                                     fy=zoom_in_scale)
            cv2.drawMarker(img_zoom_in, (zoom_in_range * zoom_in_scale,
                                         zoom_in_range * zoom_in_scale),
                           (0, 0, 255),
                           markerType=cv2.MARKER_CROSS, markerSize=30,
                           thickness=2, line_type=cv2.LINE_AA)
            height, width, depth = np.shape(img_zoom_in)

            img[y:y + height, x:x + width] = img_zoom_in
            cv2.rectangle(img, (x, y), (x + height, y + width),
                          (0, 0, 255), thickness=2)


    if event == cv2.EVENT_LBUTTONDOWN:
        # search for nearest point
        node = closest_node((x, y), pts)
        if(node >=0):
            update_img(node)

    if event == cv2.EVENT_LBUTTONUP:
        node = closest_node((x, y), pts)
        if (node >= 0):
            update_img(node, button_up=True)
        node = -1

    if event == cv2.EVENT_MOUSEMOVE:
        # redraw figure
        if (node != -1):
            update_img(node)

draw_landmarks(img, pts)

cv2.namedWindow("img", cv2.WINDOW_NORMAL)
cv2.setMouseCallback("img", click_adjust_wireframe)

while(True):
    cv2.imshow('img', img)
    key = cv2.waitKey(1)
    if key == ord("q"):
        break
cv2.destroyAllWindows()

print('vis and refine landmark Done!')
pts = np.concatenate([pts, np.ones((68, 1))], axis=1)
np.savetxt(os.path.join(data_dir, '{}_face_open_mouth.txt'.format(CH)), pts, fmt='%.4f')


''' =================================================================
            find closed mouth landmark and normalize
    
Input: param are used to change closed mouth strength
        param[0]: larger -> outer-upper lip higher 
        param[1]: larger -> outer-lower lip higher 
        param[2]: larger -> inner-upper lip higher 
        param[3]: larger -> inner-lower lip higher 

Output: saved as CH_face_open_mouth_norm.txt
                 CH_scale_shift.txt
                 CH_face_close_mouth.txt  
                 
Press Q or close the image window to continue.
================================================================= '''


norm_anno(data_dir, CH, param=[0.7, 0.4, 0.5, 0.5], show=True)


''' =================================================================
                       delauney tri
    
Input: INNER_ONLY indicates whether use the inner lip landmarks only

Output: saved as CH_delauney_tri.txt

Press any key to continue.
================================================================= '''


delauney_tri(data_dir, test_data, INNER_ONLY=False)