Initial commit

.gitignore

+data/*

README.md

+## Prerequisites
+Run the following command
+~~~~
+$ make
+~~~~
+
+Will get the data needed for this tool.
+
+## References
+1. [imm_dataset](http://www.imm.dtu.dk/~aam/datasets/datasets.html, "Imm dataset")

data/README.md

+## Download the imm database
+
+    wget http://www.imm.dtu.dk/~aam/datasets/imm_face_db.tar.gz && tar -xvzf imm_face_db.tar.gz

makefile

+all: data
+
+data: data/imm_face_db
+
+data/imm_face_db: data/imm_face_db.tar.gz
+	(cd data; \
+		mkdir -p imm_face_db; \
+		tar -xvzf imm_face_db.tar.gz -C imm_face_db \
+	)
+
+data/imm_face_db.tar.gz:
+	(cd data; wget http://www.imm.dtu.dk/~aam/datasets/imm_face_db.tar.gz)

src/imm_points.py

+from matplotlib.tri import Triangulation
+import cv2
+import numpy as np
+import argparse
+
+
+class IMMPoints():
+    """Accepts IMM datapoint file which can be shown or used"""
+    def __init__(self, filename=None, points=None):
+        """
+        Args:
+            filename: optional .asf file with the imm format
+            points: optional list of x,y points
+        """
+        self.points = points if points is not None else []
+        self.filename = filename
+
+        if filename:
+            self.import_file(filename)
+
+    def get_points(self):
+        return self.points
+
+    def import_file(self, filename):
+        with open(filename, 'r') as f:
+            lines = f.readlines()
+            # store the filename we've got
+            self.filename = lines[-1].strip()
+            data = lines[16:74]
+
+            for d in data:
+                self.points.append(d.split()[2:4])
+
+        self.points = np.asarray(self.points, dtype='f')
+
+    def draw_triangles(self, img, points):
+        assert(len(self.points) > 0)
+        h, w, c = img.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(img, tuple(p1), tuple(p2), (255, 0, 100), 1)
+            cv2.line(img, tuple(p2), tuple(p3), (255, 0, 100), 1)
+            cv2.line(img, tuple(p3), tuple(p1), (255, 0, 100), 1)
+
+        for i, p in enumerate(points):
+            point_index = int(point_indices[i])
+            cv2.putText(img, str(point_index), tuple((p[0], p[1])),
+                        cv2.FONT_HERSHEY_SIMPLEX, .5, (100, 0, 255))
+            cv2.circle(img, tuple(p), 3, color=(0, 255, 100))
+
+    def show_on_img(self, img):
+        self.draw_triangles(img, self.points)
+        cv2.imshow('image', img)
+        cv2.waitKey(0)
+
+    def show(self):
+        """show the image and datapoints on the image"""
+        assert(len(self.points) > 0)
+        assert(len(self.filename) > 0)
+
+        img = cv2.imread('data/imm_face_db/' + self.filename)
+
+        self.draw_triangles(img, self.points)
+
+        cv2.imshow('image', img)
+        cv2.waitKey(0)
+
+
+def add_parser_options():
+    parser = argparse.ArgumentParser(description='IMMPoints tool')
+
+    # asf files
+    parser.add_argument(
+        'asf', type=str, nargs='+', help='asf files to process'
+    )
+
+    return parser
+
+if __name__ == '__main__':
+    parser = add_parser_options()
+    args = parser.parse_args()
+
+    for f in args.asf:
+        imm = IMMPoints(f)
+        imm.show()

src/main.py

+from pca import pca
+import numpy as np
+import argparse
+
+from imm_points import IMMPoints
+
+
+def add_parser_options():
+    parser = argparse.ArgumentParser(description='IMMPoints tool')
+
+    # asf files
+    parser.add_argument(
+        '--asf', nargs='+', help='asf files to process'
+    )
+
+    return parser
+
+
+def build_mean_aam(imm_points):
+    """ construct a mean from a matrix of x,y values
+    Args:
+        imm_points(numpy array)that follows the follwing structure:
+    observations:
+           0. [[x_0_0, y_0_0], ... , [x_0_m, y_0_m]],
+           1. [[x_1_0, y_1_0], ... , [x_1_m, y_1_m]],
+           2. [[x_2_0, y_2_0], ... , [x_2_m, y_2_m]],
+           3. [[x_3_0, y_3_0], ... , [x_3_m, y_3_m]]
+
+               ....           ....       .m...
+
+           n. [[x_4_0, y_4_0], ... , [x_n_m, y_n_m]]
+
+    Returns mean_values (numpy array)
+        This vector containts the mean values of the corresponding column, like so:
+           0. [[x_0_0, y_0_0], ... , [x_0_k, y_0_k]],
+           1. [[x_1_0, y_1_0], ... , [x_1_k, y_1_k]],
+           2. [[x_2_0, y_2_0], ... , [x_2_k, y_2_k]],
+           3. [[x_3_0, y_3_0], ... , [x_3_k, y_3_k]]
+
+               ....           ....       .....
+
+           n. [[x_4_0, y_4_0], ... , [x_n_k, y_n_k]]
+
+        mean. [[x_mean_0, y_mean_0], ... [x_mean_n, y_mean_n]]
+    """
+    mean_values = []
+
+    for i in range(imm_points.shape[1]):
+        mean_values.append(np.mean(imm_points[:, i], axis=0))
+
+    return np.array(mean_values)
+
+if __name__ == '__main__':
+    parser = add_parser_options()
+    args = parser.parse_args()
+
+    if args.asf:
+        imm_points = []
+
+        for f in args.asf:
+            imm = IMMPoints(filename=f)
+            imm_points.append(imm.get_points())
+            # imm.show()
+
+        mean_values = build_mean_aam(np.array(imm_points))
+        pca(imm_points, mean_values)
+
+        # show immpoints
+        imm = IMMPoints(points=mean_values)
+        img = np.full((480, 640, 3), 255, np.uint8)
+        imm.show_on_img(img)

src/pca.py

+def pca(data, mean_values):
+    print mean_values