Dodany plik do projektu wspólnego, zmodyfikowany podprojekt na potrzeby projektu wspólnego

Marcin.py

@@ -74,3 +74,14 @@ class main():
             return 3
         elif self.mode_value in [6]:
             return 10
+
+    def main_collective(self,poz):
+        self.pole = self.ui.field_images[self.field.get_value(poz)]
+        self.img = pygame.surfarray.array3d(self.pole)
+        self.img = self.img.transpose([1, 0, 2])
+        self.img = cv2.cvtColor(self.img, cv2.COLOR_RGB2BGR)
+        self.reco = self.mode(self.recognition(self.img))
+        if self.reco == 10:
+            print("Nic nie trzeba robić")
+        else:
+            self.traktor.set_mode(self.reco)

collective.py

@@ -0,0 +1,15 @@
+class main():
+
+    def __init__(self,marcin, justyna, kamila, piotrek, traktor, pole, UI, path):
+        self.marcin = marcin
+        self.justyna = justyna
+        self.kamila = kamila
+        self.piotrek = piotrek
+        self.traktor = traktor
+        self.pole = pole
+        self.UI = UI
+        self.path = path
+
+    def main(self):
+        # self.marcin.main_collective(pozycja)  To podaje jakie jest pole jest na danym miejscu
+        pass

main.py

@@ -1,5 +1,5 @@
 import pygame, sys
-import tractor, pathfinding, field, ui, Justyna, Kamila, Marcin, Piotrek, pathfinding_decision
+import tractor, pathfinding, field, ui, Justyna, Kamila, Marcin, Piotrek, pathfinding_decision, collective
 from pygame.locals import *
 
 pole = field.field()
@@ -10,6 +10,7 @@ j = Justyna.main(traktor,pole,UI,path)
 k = Kamila.main(traktor,pole,UI,pathfinding_decision.pathfinding_dec())
 neuro = Marcin.main(traktor,pole,UI,path)
 p = Piotrek.main(traktor,pole,UI,path)
+c = collective.main(neuro,j,k,p,traktor,pole,UI,path)
 pygame.init()
 UI.update()
 UI.update()
@@ -41,12 +42,6 @@ while True:
                 traktor.set_mode(3)
             if key[K_p]:
                 path.pathfinding(traktor,pole,UI)
-            if key[K_F9]:
-                print(pole.if_value(traktor.get_modes_values()))
-            if key[K_F10]:
-                print(traktor.get_poz())
-            if key[K_F11]:
-                print(traktor.get_field_value())
             if key[K_F5]:
                 #Dla projektu Justyny
                 j.main()
@@ -59,6 +54,15 @@ while True:
             if key[K_F8]:
                 # Dla projektu Piotrka
                 p.main()
+            if key[K_F9]:
+                print(pole.if_value(traktor.get_modes_values()))
+            if key[K_F10]:
+                print(traktor.get_poz())
+            if key[K_F11]:
+                print(traktor.get_field_value())
+            if key[K_12]:
+                c.main()
+
             UI.update()
             UI.update()
 

si.cfg

@@ -0,0 +1,182 @@
+[net]
+# Testing
+batch=24
+subdivisions=8
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=42
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=9
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 8
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=42
+activation=linear
+
+[yolo]
+mask = 0,1,2
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=9
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1

si.names

@@ -0,0 +1,9 @@
+0
+1
+2
+3
+4
+5
+6
+7
+8