Add a way of altering the amount of shape components needed for the reconstruction.

src/aam.py

@@ -82,6 +82,30 @@ class AAMPoints():
 
         return self.points_list
 
+    def draw_triangles(self, image=None, show_points=True):
+        assert self.points_list is not None, \
+            'the list points already need to be scaled order to correctly work,\
+            this requires that get_scaled_points is executed first.'
+
+        if image is None:
+            image = self.image
+
+        triangles = Triangulation(self.points_list[:, 0], self.points_list[:, 1])
+
+        for t, tri in enumerate(triangles.triangles):
+            p1, p2, p3 = self.points_list[tri]
+            cv2.line(image, tuple(p1), tuple(p2), (255, 0, 100), 1)
+            cv2.line(image, tuple(p2), tuple(p3), (255, 0, 100), 1)
+            cv2.line(image, tuple(p3), tuple(p1), (255, 0, 100), 1)
+
+        if show_points:
+            point_indices = list(range(0, max(self.actual_shape)))
+            for i, p in enumerate(self.points_list):
+                point_index = int(point_indices[i])
+                cv2.putText(image, str(point_index), tuple((p[0], p[1])),
+                            cv2.FONT_HERSHEY_SIMPLEX, .5, (100, 0, 255))
+                cv2.circle(image, tuple(p), 3, color=(0, 255, 100))
+
     def calculate_bounding_box(self):
         """
         Calculate bounding box in the **scaled** points list

src/datasets/imm.py

@@ -92,27 +92,6 @@ class IMMPoints(aam.AAMPoints):
 
         return np.asarray(points_list, dtype='f')
 
-    def draw_triangles(self, image, points, multiply=True):
-        if multiply:
-            h, w, c = image.shape
-
-            points[:, 0] = points[:, 0] * w
-            points[:, 1] = points[:, 1] * h
-
-        point_indices = list(range(0, 58))
-        triangles = Triangulation(points[:, 0], points[:, 1])
-
-        for t, tri in enumerate(triangles.triangles):
-            p1, p2, p3 = points[tri]
-            cv2.line(image, tuple(p1), tuple(p2), (255, 0, 100), 1)
-            cv2.line(image, tuple(p2), tuple(p3), (255, 0, 100), 1)
-            cv2.line(image, tuple(p3), tuple(p1), (255, 0, 100), 1)
-
-        for i, p in enumerate(points):
-            point_index = int(point_indices[i])
-            cv2.putText(image, str(point_index), tuple((p[0], p[1])),
-                        cv2.FONT_HERSHEY_SIMPLEX, .5, (100, 0, 255))
-            cv2.circle(image, tuple(p), 3, color=(0, 255, 100))
 
     def show_on_image(self, image, window_name='image', multiply=True):
         self.draw_triangles(image, self.points_list, multiply=multiply)

src/main.py

@@ -2,6 +2,7 @@
 # python std
 import argparse
 import importlib
+import copy
 
 # installed packages
 import cv2
@@ -207,21 +208,35 @@ def show_reconstruction(args):
     mean_points = dataset_module.IMMPoints(points_list=shape_model.mean_values)
     mean_points.get_scaled_points(input_image.shape)
 
+    n_components = 58
+    count = 0
+
     while True:
+        input_image_copy = input_image.copy()
+        input_points_copy = copy.deepcopy(input_points)
+
+        reconstruction.reconstruct_shape(
+            input_image_copy, input_points_copy, shape_model,
+            n_components=n_components - count
+        )
+
         reconstruction.reconstruct_texture(
-            input_image,  # src image
-            input_image,  # dst image
+            input_image_copy,  # src image
+            input_image_copy,  # dst image
             texture_model,
-            input_points,  # shape points input
-            mean_points,   # shape points mean
+            input_points_copy,  # shape points input
+            mean_points    # shape points mean
         )
 
+        input_points_copy.get_scaled_points(input_image.shape)
+        input_points_copy.draw_triangles(image=input_image_copy, show_points=False)
+
         dst = reconstruction.get_texture(
-                mean_points, texture_model.mean_values
-            )
+            mean_points, texture_model.mean_values
+        )
 
-        cv2.imshow('original', input_points.get_image())
-        cv2.imshow('reconstructed', input_image)
+        cv2.imshow('original', input_image)
+        cv2.imshow('reconstructed', input_image_copy)
         cv2.imshow('main face', dst)
 
         k = cv2.waitKey(0) & 0xFF
@@ -229,6 +244,8 @@ def show_reconstruction(args):
         if k == 27:
             break
 
+        count += 2
+
     cv2.destroyAllWindows()
 
 

src/pca.py

@@ -153,12 +153,19 @@ def reconstruct(feature_vector, Vt, mean_values, n_components=None):
 
     """
 
+    # if n_components is None, use all components.
     if n_components is None:
         n_components = Vt.shape[1]
 
+    # create the zero mean, is always needed for PCA reconstruction.
+    # we can only alter the data in zero_mean.
     zm = feature_vector - mean_values
+
+    # project the zero mean onto the the VT bases (with potentially less
+    # amount of dimensions than the original.
     yk = np.dot(Vt[:n_components], zm.T)
 
+    # project back to the original and add the mean_values again.
     return np.dot(Vt[:n_components].T, yk) + mean_values
 
 

src/reconstruction/reconstruction.py

@@ -78,45 +78,79 @@ def get_texture(Points, flattened_texture):
     return np.asarray(flattened_texture, np.uint8).reshape((h_slice, w_slice, 3))
 
 
+def reconstruct_shape(image, points, shape_model, n_components=None):
+    input_points = points.get_points()
+    mean_points = shape_model.mean_values
+
+    reconstructed = pca.reconstruct(
+            input_points,
+            shape_model.Vt,
+            mean_points,
+            n_components=n_components
+            )
+
+    points.normalized_flattened_points_list = reconstructed
+
+
 def reconstruct_texture(src_image, dst_image, texture_model, src_points, dst_points):
     """
     Recontruct texture given the src and dst image
 
     Args:
-        src_points(aam.AAMPoints)
-        dst_points(aam.AAMPoints)
+        src_image(ndarray): numpy / OpenCV image in BGR
+        dst_image(ndarray): numpy image / OpenCVImage, may be None, if None
+        then we create an image just as big a the input image but then with a
+        black background.
+        texture_model(PCAModel): The PCAModel that holds the information that
+        we need to reconstruct the image, see pca module.
+        Make one by doing this: (see PCAModel on how it is stored in numpy
+        file).
+        texture_model = pca.PCAModel(model_texture_file)
+        src_points(aam.AAMPoints): The AAMPoints object contains the location
+        of the landmarks on the face that we need to perform piece wise affine
+        warping.
+        dst_points(aam.AAMPoints): The AAMPoints object contains the location
+        of the landmarks on the face that we need to perform piece wise affine
+        warping.
     """
     Vt = texture_model.Vt
     triangles = texture_model.triangles
     mean_texture = texture_model.mean_values
-    # n_components = texture_model.n_components
 
-    # S_mean format
     h, w, c = src_image.shape
+
+    # empty input_texture
     input_texture = np.full((h, w, 3), fill_value=0, dtype=np.uint8)
 
     points2d_src = src_points.get_scaled_points(src_image.shape)
     points2d_dst = dst_points.get_scaled_points(dst_image.shape)
 
+    # get the texture from the rectangles.
     aam.sample_from_triangles(
         src_image,
         points2d_src,
         points2d_dst,
         triangles,
-        input_texture
+        input_texture  # this will be filled with the texture afterwards.
     )
 
+    # define the rectangle around the dst_points.
     offset_x, offset_y, w_slice, h_slice = dst_points.get_bounding_box()
+
+    # cut out this region from the input_texture.
     input_texture = input_texture[offset_y: offset_y + h_slice,
                                   offset_x: offset_x + w_slice].flatten()
 
-    # Still in  S_mean format
+    # perfrom the PCA reconstruction using the input_texture.
     r_texture = pca.reconstruct(input_texture, Vt, mean_texture)
 
-    # Make an image from the float data
+    # Make an image from the data, the texture is still of type `float`.
     r_texture = np.asarray(r_texture, np.uint8).reshape((h_slice, w_slice, 3))
 
-    # subtract the offset
+    # subtract the offset, this is needed because the image is now a
+    # small rectangle around the face which starts at [0,0], wheras it first
+    # was located at offset_x, offset_y. We need both rectangles to start at
+    # [0, 0]. Please note that this should be improved to avoid confusion.
     points2d_dst[:, 0] -= offset_x
     points2d_dst[:, 1] -= offset_y
 

src/server.py

@@ -4,11 +4,13 @@ import base64
 from glob import glob
 
 import cv2
+import numpy as np
 from tornado import websocket, web, ioloop, autoreload
 
 import pca
 from datasets import imm
 from reconstruction import reconstruction
+from settings import logger
 
 BASE = '../viewer/app'
 FILES_DIR = '../data/'
@@ -19,7 +21,7 @@ FACE_DB = '{}{}'.format(FILES_DIR, FACE_DB_NAME)
 class ImageWebSocketHandler(websocket.WebSocketHandler):
     handlers = {
         'filename': 'handle_return_image',
-        'reconstruction_index': 'handle_return_reconstruction'
+        'reconstruction': 'handle_return_reconstruction'
     }
 
     def __init__(self, *args, **kwargs):
@@ -57,6 +59,12 @@ class ImageWebSocketHandler(websocket.WebSocketHandler):
     def handle_return_reconstruction(self, message):
         """ Return the reconstruction of the given image """
         image_index = message['reconstruction_index']
+        image_as_background = message.get('background_image', True)
+        shape_components = message.get('shape_components', 58)
+        print message
+
+        logger.info('using %s shape_components', shape_components)
+
         asf_filename = self.asf[image_index]
 
         input_points = imm.IMMPoints(filename=asf_filename)
@@ -65,16 +73,29 @@ class ImageWebSocketHandler(websocket.WebSocketHandler):
         mean_points = imm.IMMPoints(points_list=self.shape_model.mean_values)
         mean_points.get_scaled_points(input_image.shape)
 
-        #TODO This one is VERY SLOW, try to optimize
+        # set dst image to an empty image if value is None
+        if image_as_background is False:
+            h, w, _ = input_image.shape
+            dst_image = np.full((h, w, 3), fill_value=0, dtype=np.uint8)
+        else:
+            dst_image = input_image
+
+        reconstruction.reconstruct_shape(
+            input_image, input_points, self.shape_model,
+            n_components=shape_components
+        )
+
         reconstruction.reconstruct_texture(
             input_image,  # src image
-            input_image,  # dst image
+            dst_image,  # dst image
             self.texture_model,
             input_points,  # shape points input
-            mean_points,   # shape points mean
+            mean_points   # shape points mean
         )
 
-        _, reconstructed = cv2.imencode('.jpg', input_image)
+        input_points.draw_triangles(show_points=False)
+
+        _, reconstructed = cv2.imencode('.jpg', dst_image)
         reconstructed = base64.b64encode(reconstructed)
 
         self.write_message(json.dumps({'reconstructed': reconstructed}))
@@ -96,7 +117,7 @@ class ImageWebSocketHandler(websocket.WebSocketHandler):
                 print(msg, e)
                 self.__return_error(msg)
 
-            handler(message)
+            handler(message[m])
 
     def on_close(self):
         print("WebSocket closed")

viewer/app/components/three-js-reconstruction.js

@@ -8,13 +8,10 @@ const ThreeComponent = Ember.Component.extend({
     scene: null,
 
     willRender() {
-        if (this.scene) {
-            return;
-        }
+        if (this.scene) { return; }
 
         var scene = new THREE.Scene();
         var gui = new dat.GUI();
-
         var camera = new THREE.PerspectiveCamera(
             75, window.innerWidth / window.innerHeight, 0.1, 1000
         );
@@ -23,10 +20,25 @@ const ThreeComponent = Ember.Component.extend({
 
         var renderer = new THREE.WebGLRenderer();
 
+        // the sidebar 'dat-gui' controls
+        var reconstructionControls = {
+            index: 0,
+            shape_components: 58,
+            background_image: true,
+        };
+
+        for(var i = 0; i < 15; i++) {
+            reconstructionControls['shape_eigen_value_' + i] = 0.0;
+        }
+
+        var shapeEigenValueSliders = {};
+
         this.set('scene', scene);
         this.set('camera', camera);
         this.set('renderer', renderer);
         this.set('gui', gui);
+        this.set('controls', reconstructionControls);
+        this.set('shapeEigenValueSliders', shapeEigenValueSliders);
 
         this.get('store').findAll('face').then((faces) => {
             this.set('faces', faces);
@@ -45,29 +57,47 @@ const ThreeComponent = Ember.Component.extend({
 
     }),
 
+    /**
+     * Adds the 'dat-gui' sliders
+     *
+     * See:
+     * http://learningthreejs.com/blog/2011/08/14/dat-gui-simple-ui-for-demos/
+     */
     addSliders() {
         var self = this;
         var gui = this.get('gui');
-        var obj = {
-            name: "Image filename",
-            index: 0
-        };
-
-        var components = {
-            name: "Components",
-            components: 0
-        };
+        var reconstructionControls = this.get('controls');
+        var shapeEigenValueSliders = this.get('shapeEigenValueSliders');
 
         var length = this.get('faces').get('length');
 
-        var imagesSlider = gui.add(obj, "index").min(0).max(
-                length - 1).step(1);
+        var index = gui.add(reconstructionControls, 'index', 0, length - 1);
+        var shape_components = gui.add(reconstructionControls, 'shape_components', 0, 58);
+
+        var background = gui.add(reconstructionControls, 'background_image');
+        var shapeEigenValueControls = gui.addFolder('shape_eigen_values');
+
+        for(var i = 0; i < 15; i++) {
+            shapeEigenValueControls.add(reconstructionControls, 'shape_eigen_value_' + i, 0.0, 10.0);
+        }
+
+        // on index change
+        index.onChange(function(newValue) {
+            // update the image_index, which is on the controller
+            self.set('image_index', parseInt(newValue));
+            self.sendAction('updateIndex', parseInt(newValue));
+        });
 
-        gui.add(components, "components").min(0).max(length - 1).step(1);
+        background.onChange(function(newValue) {
+            self.sendAction('updateBackground', newValue);
+        });
+
+        shape_components.onChange(function(newValue) {
+            self.sendAction('updateShapeComponents', newValue);
+        });
 
-        imagesSlider.onChange(function(newValue) {
-            self.set('image_index', newValue);
-            self.sendAction('update', newValue);
+        reconstructionControls.onChange(function(newValue) {
+            console.log(newValue);
         });
     }
 });

viewer/app/controllers/reconstruction.js

@@ -10,7 +10,8 @@ export default Ember.Controller.extend({
     image: null,
 
     image_index: 0,
-    n_components: null,
+    background_image: true,
+    shape_components: null,
     n_images: null,
     reconstructed: null,
 
@@ -64,7 +65,8 @@ export default Ember.Controller.extend({
         this.set('loading', false);
     },
 
-    getReconstruction: Ember.observer('image_index', function() {
+    getReconstruction: Ember.observer(
+            'image_index', 'background_image', 'shape_components', function() {
         this.send('getReconstruction');
     }),
 
@@ -75,7 +77,6 @@ export default Ember.Controller.extend({
     actions: {
         getImage(faceModel) {
             this.set('loading', true);
-
             var filename = faceModel.get('filename');
             const socket = this.get('socketRef');
 
@@ -86,18 +87,33 @@ export default Ember.Controller.extend({
 
         getReconstruction() {
             this.set('loading', true);
-
             const socket = this.get('socketRef');
 
             socket.send(
-                JSON.stringify({reconstruction_index: this.get('image_index')}
+                JSON.stringify({
+                    reconstruction: {
+                        reconstruction_index: this.get('image_index'),
+                        background_image: this.get('background_image'),
+                        shape_components: this.get('shape_components')
+                    }
+
+                }
             ));
         },
 
         // connects components together
         // handles the upate action passed to a component
-        updateComponentConnector(index) {
+        updateIndexComponentConnector(index) {
             this.set('image_index', index);
+        },
+
+        updateBackgroundComponentConnector(showBackground) {
+            this.set('background_image', showBackground);
+        },
+
+        updateShapeComponents(components) {
+            console.log('shape_components', components);
+            this.set('shape_components', components);
         }
     }
 });

viewer/app/templates/components/images-loader.hbs

 {{# if current_face_filename }}
     <img src='{{current_face_filename}}' alt='missing original'>
-{{else}}
-<div class="table">
-    <div class="table-cell align-middle">
-        {{fa-icon "spinner" spin=true size='lg'}}
-        Loading..
-    </div>
-</div>
 {{/if}}
-
 {{yield}}

viewer/app/templates/reconstruction.hbs

@@ -3,25 +3,34 @@
 <div class="container">
     <div class="clearfix">
         <div class="col col-6">
-            {{images-loader image_index=image_index}}
+            {{images-loader image_index=image_index
+                background_image=background_image
+                shape_components=shape_components
+                loading=loading
+            }}
         </div>
 
         <div class="col col-6">
-            {{#if loading }}
-                <div class="table">
-                    <div class="table-cell align-middle">
-                        {{fa-icon "spinner" spin=true size='lg'}}
-                        Loading..
-                    </div>
+            <div class="table">
+                <div class="table-cell align-middle">
+                    {{#if reconstructed }}
+                        <img src='data:image/jpg;base64,{{reconstructed}}'
+                            alt='missing image'>
+                    {{/if}}
+
+                    {{#if loading }}
+                        {{fa-icon "spinner" spin=true size='lg'}} Loading..
+                    {{/if}}
                 </div>
-            {{else if reconstructed }}
-                <img src='data:image/jpg;base64,{{reconstructed}}'
-                    alt='missing image'>
-            {{/if}}
+            </div>
         </div>
     </div>
 
     <div class="col col-12 px2">
-        {{three-js-reconstruction update=(action 'updateComponentConnector')}}
+        {{three-js-reconstruction
+            updateIndex=(action 'updateIndexComponentConnector')
+            updateBackground=(action 'updateBackgroundComponentConnector')
+            updateShapeComponents=(action 'updateShapeComponents')
+        }}
     </div>
 </div>