Release v2.1 pushed

README.md

@@ -1,6 +1,32 @@
 # Blob Simulation & Detection
 Détection d'un blob dans une image ainsi que détection des comprimés de nourriture. Simulation d'un blob dans PyGame à travers un système multi-agents à connaissance partagée.
 
+## Release 2.1 - *7/06/2019*
+
+### Modifications : 
++ Déplacement des scripts dans le dossier root
++ Changement du nom des fichiers enregistrés par la détection
++ Automatisation des différents scripts, ajout d'un fonctionnement en mode "caché".
++ Automatisation de la simulation avec ajouts de paramètres et sauvegarde finale
++ Ajout d'un paramètre pour initialiser de la nourriture dans la simulation
++ Ajout d'un paramètre d'affichage dans la simulation
++ Uniformisation des paramètres dans les différents scripts
++ Possibilité de raffiner un modèle détecté avec un fichier d'informations supplémentaires
++ Variables de couleur déplacées dans un fichier json séparé
++ Ajout de fichiers d'exemples pour les différents scripts
+
+### Scripts : 
++ Setup : `python setup.py -i data/example.jpg`
++ Detection : `python detect.py -i data/example.jpg`
++ Simulation : `python play.py -i data/output-examples/example-detect.board -s 3`
++ Compare : `python compare.py --first data/output-examples/simulation/10_loops/10_loops.board --second data/output-examples/simulation/100_loops/100_loops.board -s 3`
+
+Les différentes sorties produites sont les suivantes : 
+![Sortie du script de détection](data/output-examples/example-detect-details.jpg?raw=true "Sortie du script de détection") 
+![Sortie de la simulation après 100 itérations](data/output-examples/simulation/100_loops/100_loops.jpg?raw=true "Sortie de la simulation après 100 itérations")
+![Sortie du script de comparaison](data/compare_init_with_100.jpg?raw=true "Sortie du script de comparaison") 
+
+
 ## Release 2.0 - *27/05/2019*
 
 Le passage à cette release invalide les fichiers de sauvegarde précédents, le format ayant été modifié.
@@ -17,7 +43,6 @@ La nouvelle interface de la simulation produira des images comme celle-ci :
 
 ![Interface de simulation](example/test-run.jpg?raw=true "Interface de simulation") 
 
-
 Pour exécuter le script de comparaison sur un exemple : 
 `python compare.py --first example/test.board --second example/test-run.board`
 

compare.py

@@ -17,46 +17,44 @@
 import argparse
 from simulation import board
 import pygame
-from pygame.locals import QUIT
+from pygame.locals import QUIT, KEYDOWN, K_ESCAPE
 
 
 def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--first", required=True, help="first board file")
     ap.add_argument("--second", required=True, help="second board file")
-    ap.add_argument("-s", "--scale", type=float, default=10, help="scale display (default: x10")
-    ap.add_argument("-o", "--output", type=str, help="give a name to save the jpeg file")
+    ap.add_argument("-s", "--scale", type=float, default=10,
+                    help="Scales board resolution by this factor (default: x10)")
+    ap.add_argument("-o", "--output", type=str, help="Give a name to save the jpeg file")
     args = ap.parse_args()
 
-    board_1 = board.Board(0,0)
+    board_1 = board.Board(0, 0)
     board_1.load(args.first)
 
-    board_2 = board.Board(0,0)
+    board_2 = board.Board(0, 0)
     board_2.load(args.second)
 
     board_comp = board_1.compare(board_2)
 
     if board_comp is None:
         return
 
-    pygame.init()
-
     width = int(board_1.width * args.scale)
     height = int(board_1.height * args.scale)
-    window = pygame.display.set_mode((width, height))
 
     game_surface = pygame.Surface((board_1.width, board_1.height))
     pixel_array = pygame.PixelArray(game_surface)
     for x in range(board_1.width):
         for y in range(board_1.height):
-            pixel_array[x,y] = (0, 0, 0)
+            pixel_array[x, y] = (0, 0, 0)
 
             val = max(-255, min(board_comp.get_blob(x, y), 255))
             if val < 0:
-                val = - val # int(-val/2) + 125
+                val = - val  # int(-val/2) + 125
                 pixel_array[x, y] = (val/4, val/4, val)
             elif val > 0:
-                val = val # int(val/2) + 125
+                val = val  # int(val/2) + 125
                 pixel_array[x, y] = (val, val/4, val/4)
             else:
                 if not board_comp.is_touched(x, y):
@@ -66,24 +64,27 @@ def main():
                         pixel_array[x, y] = (125, 75, 75)
 
             if board_comp.has_food(x, y):
-                 pixel_array[x, y] = (0, board_comp.foods[x, y], 0)
+                pixel_array[x, y] = (0, board_comp.foods[x, y], 0)
 
     del pixel_array
 
     game_window = pygame.transform.scale(game_surface, (width, height))
 
-    window.blit(game_window, (0, 0))
-    pygame.display.flip()
-
-    ended = False
-    while not ended:
-        pygame.time.wait(10)
-        for event in pygame.event.get():
-            if event.type == QUIT:
-                ended = True
-
     if args.output is not None:
-        pygame.image.save(window, args.output)
+        pygame.image.save(game_window, args.output)
+
+    else:
+        pygame.init()
+        window = pygame.display.set_mode((width, height))
+        window.blit(game_window, (0, 0))
+        pygame.display.flip()
+
+        ended = False
+        while not ended:
+            pygame.time.wait(10)
+            for event in pygame.event.get():
+                if event.type == QUIT or event.type == KEYDOWN and event.key == K_ESCAPE:
+                    ended = True
 
 
 if __name__ == "__main__":

example/test-run.player.json → data/output-examples/example-detect.player.json

@@ -1,5 +1,5 @@
 {
     "clean_top": true,
-    "food_size": 5,
+    "food_size": 6,
     "use_food_circle": true
 }

data/output-examples/refined/example-detect.player.json

@@ -0,0 +1,5 @@
+{
+    "clean_top": false,
+    "food_size": 6,
+    "use_food_circle": true
+}

data/output-examples/simulation/100_loops/100_loops.blob.json

@@ -0,0 +1,32 @@
+{
+    "Computing": {
+        "Blob Size Factor": 0.25,
+        "Covering Factor": 0.75,
+        "Global Factor": 2.78,
+        "Known Foods Factor": 0.05
+    },
+    "Gathering": {
+        "Diagonal Moves": true,
+        "Light Compute": true,
+        "Sightline": -1
+    },
+    "Global Decrease": 0.1,
+    "Harvesting": {
+        "Collect": 10,
+        "Eat": 5,
+        "Max": 30,
+        "Min": 30
+    },
+    "Remaining Blob on Food": 50,
+    "Scouters": {
+        "Drop by eat": 25,
+        "Min": 2
+    },
+    "Scouting": {
+        "Diagonal Moves": true,
+        "Global Explore Probability": 0.02,
+        "Light Compute": true,
+        "Search Locally on Food": true,
+        "Sightline": 3
+    }
+}

example/test.player.json → data/output-examples/simulation/100_loops/100_loops.player.json

@@ -1,5 +1,5 @@
 {
     "clean_top": true,
-    "food_size": 5,
+    "food_size": 6,
     "use_food_circle": true
 }

data/output-examples/simulation/100_loops/100_loops.results.json

@@ -0,0 +1,10 @@
+{
+    "Covering": {
+        "Bottom": 34.74375,
+        "Top": 9.35,
+        "Total": 22.046875
+    },
+    "From": "save/example-detect",
+    "Loops": 100,
+    "To": "save/2019-06-07_16.26.50"
+}

data/output-examples/simulation/10_loops/10_loops.blob.json

@@ -0,0 +1,32 @@
+{
+    "Computing": {
+        "Blob Size Factor": 0.25,
+        "Covering Factor": 0.75,
+        "Global Factor": 2.78,
+        "Known Foods Factor": 0.05
+    },
+    "Gathering": {
+        "Diagonal Moves": true,
+        "Light Compute": true,
+        "Sightline": -1
+    },
+    "Global Decrease": 0.1,
+    "Harvesting": {
+        "Collect": 10,
+        "Eat": 5,
+        "Max": 30,
+        "Min": 30
+    },
+    "Remaining Blob on Food": 50,
+    "Scouters": {
+        "Drop by eat": 25,
+        "Min": 2
+    },
+    "Scouting": {
+        "Diagonal Moves": true,
+        "Global Explore Probability": 0.02,
+        "Light Compute": true,
+        "Search Locally on Food": true,
+        "Sightline": 3
+    }
+}

data/output-examples/simulation/10_loops/10_loops.player.json

@@ -0,0 +1,5 @@
+{
+    "clean_top": true,
+    "food_size": 6,
+    "use_food_circle": true
+}

data/output-examples/simulation/10_loops/10_loops.results.json

@@ -0,0 +1,10 @@
+{
+    "Covering": {
+        "Bottom": 33.6875,
+        "Top": 6.809375000000001,
+        "Total": 20.2484375
+    },
+    "From": "save/example-detect",
+    "Loops": 10,
+    "To": "save/10_loops"
+}

data/refine-example.json

@@ -0,0 +1,12 @@
+{
+	"Width": 100,
+	"Height": 40,
+	"Foods": [
+		[10, 10],
+		[18,28],
+		[23, 8],
+		[44,23],
+		[96,22]
+	],
+	"Clean Top": false
+}

detect.py

@@ -0,0 +1,64 @@
+# Copyright (C) 2019 - UMons
+# 
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+# 
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+# 
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+
+import argparse
+import json
+from detection.detection import detect, discretize, print_results
+from detection.refine import simulate, save
+from os.path import splitext, basename, join
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--input", required=True, help="Uses this input as image for detection")
+    ap.add_argument("-s", "--scale", type=float, default=0.10, help="Scales images by this factor (default: x0.1)")
+    ap.add_argument("-c", "--config", type=str, default="detection/config.json",
+                    help="Loads config from this file (default: detection/config.json)")
+    ap.add_argument("--save", type=str, default="save/",
+                    help="Pass the directory where saves are stored. (default: save/)")
+    ap.add_argument("--hide", action='store_true', default=False, help="Hide images if parameter is set")
+    ap.add_argument("--refine", type=str, help="Pass a json file to refine model")
+
+    args = ap.parse_args()
+    with open(args.config, 'r') as file:
+        config = json.load(file)
+
+    if args.refine is not None:
+        with open(args.refine, 'r') as file:
+            refine = json.load(file)
+    else:
+        refine = None
+
+    orig, blob_mask, blob, food_mask, food_img = detect(args.input, config)
+    dsc_img, dsc_blob, dsc_food_list = discretize(blob, food_mask, config['Discrete Width'], config['Discrete Height'])
+
+    if args.save is not None:
+        filename = splitext(basename(args.input))[0] + "-detect"
+        file_path = join(args.save, filename)
+
+        board, player, img = simulate(dsc_img, dsc_blob, dsc_food_list, config, refine)
+        save(file_path, board, player, img)
+
+        # Prepare file_path for details if any to save
+        file_path += "-details"
+    else:
+        file_path = None
+
+    print_results(orig, blob_mask, blob, food_mask, food_img, dsc_img, args.scale, file_path, args.hide)
+
+
+if __name__ == "__main__":
+    main()

detection/config.json

@@ -1,22 +1,34 @@
 {
     "Aspect Ratio": 0.4,
-    "Discrete Height": 240,
-    "Discrete Width": 600,
+    "Discrete Height": 160,
+    "Discrete Width": 400,
     "High Food Color": [
         214,
         230,
         237
     ],
     "Limits": [
-		[136, 268],
-		[5484, 208],
-		[5452, 3296], 
-		[136, 3308]
-	],
+        [
+            136,
+            268
+        ],
+        [
+            5484,
+            208
+        ],
+        [
+            5452,
+            3296
+        ],
+        [
+            136,
+            3308
+        ]
+    ],
     "Low Food Color": [
         150,
         185,
         198
     ],
-	"Min Food Size": 60
+    "Min Food Size": 60
 }