add codes generating gif

examples/2_3dmm.py

@@ -1,7 +1,9 @@
 ''' 3d morphable model example
-3dmm parameters --> mesh (and inverse)
+3dmm parameters --> mesh 
+fitting: 2d image + 3dmm -> 3d face
 '''
 import os, sys
+import subprocess
 import numpy as np
 import scipy.io as sio
 from skimage import io
@@ -47,7 +49,7 @@
 X_ind = bfm.kpt_ind # index of keypoints in 3DMM. fixed.
 
 # fit
-fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(x, X_ind, max_iter = 4)
+fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(x, X_ind, max_iter = 3)
 
 # verify fitted parameters
 fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
@@ -67,3 +69,21 @@
 
 io.imsave('{}/generated.jpg'.format(save_folder), image)
 io.imsave('{}/fitted.jpg'.format(save_folder), fitted_image)
+
+
+### ----------------- visualize fitting process
+# fit
+fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(x, X_ind, max_iter = 3, isShow = True)
+
+# verify fitted parameters
+for i in range(fitted_sp.shape[0]):
+	fitted_vertices = bfm.generate_vertices(fitted_sp[i], fitted_ep[i])
+	transformed_vertices = bfm.transform(fitted_vertices, fitted_s[i], fitted_angles[i], fitted_t[i])
+
+	image_vertices = mesh.transform.to_image(transformed_vertices, h, w)
+	fitted_image = mesh_cython.render.render_colors(image_vertices, bfm.triangles, colors, h, w)
+	io.imsave('{}/show_{:0>2d}.jpg'.format(save_folder, i), fitted_image)
+
+options = '-delay 20 -loop 0 -layers optimize' # gif. need ImageMagick.
+subprocess.call('convert {} {}/show_*.jpg {}'.format(options, save_folder, save_folder + '/3dmm.gif'), shell=True)
+subprocess.call('rm {}/show_*.jpg'.format(save_folder), shell=True)

face3d/morphable_model/fit.py

@@ -209,4 +209,64 @@ def fit_points(x, X_ind, model, n_sp, n_ep, max_iter = 4):
         expression = np.reshape(expression, [int(len(expression)/3), 3]).T
         sp = estimate_shape(x, shapeMU, shapePC, model['shapeEV'][:n_sp,:], expression, s, R, t[:2], lamb = 40)
 
-    return sp, ep, s, R, t
+    return sp, ep, s, R, t
+
+
+# ---------------- fit 
+def fit_points_for_show(x, X_ind, model, n_sp, n_ep, max_iter = 4):
+    '''
+    Args:
+        x: (n, 2) image points
+        X_ind: (n,) corresponding Model vertex indices
+        model: 3DMM
+        max_iter: iteration
+    Returns:
+        sp: (n_sp, 1). shape parameters
+        ep: (n_ep, 1). exp parameters
+        s, R, t
+    '''
+    x = x.copy().T
+
+    #-- init
+    sp = np.zeros((n_sp, 1), dtype = np.float32)
+    ep = np.zeros((n_ep, 1), dtype = np.float32)
+
+    #-------------------- estimate
+    X_ind_all = np.tile(X_ind[np.newaxis, :], [3, 1])*3
+    X_ind_all[1, :] += 1
+    X_ind_all[2, :] += 2
+    valid_ind = X_ind_all.flatten('F')
+
+    shapeMU = model['shapeMU'][valid_ind, :]
+    shapePC = model['shapePC'][valid_ind, :n_sp]
+    expPC = model['expPC'][valid_ind, :n_ep]
+
+    s = 4e-04
+    R = mesh.transform.angle2matrix([0, 0, 0])
+    t = [0, 0, 0]
+    lsp = []; lep = []; ls = []; lR = []; lt = []
+    for i in range(max_iter):
+        X = shapeMU + shapePC.dot(sp) + expPC.dot(ep)
+        X = np.reshape(X, [int(len(X)/3), 3]).T
+        lsp.append(sp); lep.append(ep); ls.append(s), lR.append(R), lt.append(t)
+
+        #----- estimate pose
+        P = mesh.transform.estimate_affine_matrix_3d22d(X.T, x.T)
+        s, R, t = mesh.transform.P2sRt(P)
+        lsp.append(sp); lep.append(ep); ls.append(s), lR.append(R), lt.append(t)
+
+        #----- estimate shape
+        # expression
+        shape = shapePC.dot(sp)
+        shape = np.reshape(shape, [int(len(shape)/3), 3]).T
+        ep = estimate_expression(x, shapeMU, expPC, model['expEV'][:n_ep,:], shape, s, R, t[:2], lamb = 20)
+        lsp.append(sp); lep.append(ep); ls.append(s), lR.append(R), lt.append(t)
+
+        # shape
+        expression = expPC.dot(ep)
+        expression = np.reshape(expression, [int(len(expression)/3), 3]).T
+        sp = estimate_shape(x, shapeMU, shapePC, model['shapeEV'][:n_sp,:], expression, s, R, t[:2], lamb = 40)
+
+    # print('ls', ls)
+    # print('lR', lR)
+    return np.array(lsp), np.array(lep), np.array(ls), np.array(lR), np.array(lt)

face3d/morphable_model/morphabel_model.py

@@ -117,19 +117,26 @@ def transform_3ddfa(self, vertices, s, angles, t3d): # only used for processing
         R = mesh.transform.angle2matrix_3ddfa(angles)
         return mesh.transform.similarity_transform(vertices, s, R, t3d)
     # --------------------------------------------------- fitting
-    def fit(self, x, X_ind, max_iter = 4):
+    def fit(self, x, X_ind, max_iter = 4, isShow = False):
         ''' fit 3dmm & pose parameters
         Args:
             x: (n, 2) image points
             X_ind: (n,) corresponding Model vertex indices
             max_iter: iteration
+            isShow: whether to reserve middle results for show
         Returns:
             fitted_sp: (n_sp, 1). shape parameters
             fitted_ep: (n_ep, 1). exp parameters
             s, angles, t
         '''
-        fitted_sp, fitted_ep, s, R, t = fit.fit_points(x, X_ind, self.model, n_sp = self.n_shape_para, n_ep = self.n_exp_para, max_iter = max_iter)
-        angles = mesh.transform.matrix2angle(R)
+        if isShow:
+            fitted_sp, fitted_ep, s, R, t = fit.fit_points_for_show(x, X_ind, self.model, n_sp = self.n_shape_para, n_ep = self.n_exp_para, max_iter = max_iter)
+            angles = np.zeros((R.shape[0], 3))
+            for i in range(R.shape[0]):
+                angles[i] = mesh.transform.matrix2angle(R[i])
+        else:
+            fitted_sp, fitted_ep, s, R, t = fit.fit_points(x, X_ind, self.model, n_sp = self.n_shape_para, n_ep = self.n_exp_para, max_iter = max_iter)
+            angles = mesh.transform.matrix2angle(R)
         return fitted_sp, fitted_ep, s, angles, t