autotest: added first draft for grader mechanic

dataset_gen/src/grader/grader.py

@@ -0,0 +1,74 @@
+from random import random
+
+from grader_config import dist_grade, metric, pos_coeff, photo_coeff
+
+
+class Point:  # stub
+    def __init__(self):
+        self.x = random()
+        self.y = random()
+        self.z = random()
+
+
+class Grader:
+    def __init__(self, drones, task_id, grade_criteria):
+        self.drones = drones
+        self.task_id = task_id
+        self.grade_criteria = grade_criteria
+        self.metric = metric
+        self.pos_coeff = pos_coeff
+        self.photo_coeff = photo_coeff
+
+    def __get_distance(self, p1: Point, p2: Point):
+        x = p1.x - p2.x
+        y = p1.y - p2.y
+        z = p1.z - p2.z
+        if self.metric == "chessboard":
+            return max(abs(x), abs(y), abs(z))
+        elif self.metric == "taxicab":
+            return sum(abs(x) + abs(y) + abs(z))
+        elif self.metric != "plane":
+            print("Unknown metric, proposed euqclidean")  # raise?
+        return (x**2 + y**2 + z**2)**0.5
+
+    def __get_pos(self, drone):
+        return Point()  # stub
+
+    def __get_expected_pos(self, drone):
+        return Point()  # stub
+
+    def __get_photos_count(self, drone):
+        return int(random()*5)  # stub
+
+    def __get_expected_photos_count(self, drone):
+        return 5  # stub
+
+    def __grade_position(self):
+        total_grade = 0
+        for drone in self.drones:
+            pos = self.__get_pos(drone)
+            expected_pos = self.__get_expected_pos(drone)
+            dist = self.__get_distance(pos, expected_pos)
+            keys = list(filter(lambda key: dist <= key, dist_grade.keys()))
+            key = min(keys) if keys else None
+            grade = dist_grade[key] if key is not None else 0
+            total_grade += grade
+        return total_grade/len(self.drones)  # return avg
+
+    def __grade_photo(self):
+        total_grade = 0
+        for drone in self.drones:
+            count_photos = self.__get_photos_count(drone)
+            expected_photos = self.__get_expected_photos_count(drone)
+            grade = count_photos/expected_photos
+            total_grade += grade
+        return total_grade/len(self.drones)  # return avg
+
+    def grade(self):
+        used_pos_coeff = self.pos_coeff \
+            if "position" in self.grade_criteria else 0
+        used_photo_coeff = self.photo_coeff \
+            if "photo" in self.grade_criteria else 0
+        return (used_pos_coeff*self.__grade_position()
+                + used_photo_coeff*self.__grade_photo()) \
+            / (used_pos_coeff + used_photo_coeff)

dataset_gen/src/grader/grader_config.py

@@ -0,0 +1,7 @@
+dist_grade = {1: 1,
+              5: 0.9,
+              10: 0.7,
+              20: 0.4}
+metric = "plane"  # options: plane, chessboard, taxicab
+pos_coeff = 1
+photo_coeff = 1