product assigning.py to shelf.py; returning coordinates to shelf.py

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (AL-2020)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (AL-2020)" project-jdk-type="Python SDK" />
 </project>

.idea/wozek.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.7 (AL-2020)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (AL-2020)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

assigning.py

@@ -0,0 +1,26 @@
+import os
+
+from codes_recognizer.rocognizer import recognizer
+from board import get_shelfs
+
+def assigning(product_code_path):
+
+    code = recognizer(product_code_path)
+    shelfs = get_shelfs()
+
+    for shelf in shelfs:
+        if shelf.is_empty():
+            shelf.add_product(code)
+            c = shelf.get_coordinates()
+            break
+        else:
+            if shelf.get_product() == code:
+                shelf.add_product(code)
+                c = shelf.get_coordinates()
+                break
+
+    print("Product", product_code_path, "is on", c)
+    return c
+
+
+

board.py

@@ -1,9 +1,10 @@
 import pygame
 from settings import Settings
 from field import Field
+from shelf import Shelf
 
 settings = Settings()
-
+shelfs_o = []
 
 def create_board(screen):
     board = []
@@ -19,6 +20,7 @@ def create_board(screen):
                 if field.center_x == shelf[0] and field.center_y == shelf[1]:
                     field.is_shelf = True
                     field.image = pygame.image.load('img/shelf.png')
+                    shelfs_o.append(Shelf(x, y))
 
             row.append(field)
         board.append(row)
@@ -46,3 +48,6 @@ def draw_board(board):
             field.blitme()
 
 
+def get_shelfs():
+    return shelfs_o
+

coder.py

@@ -68,8 +68,8 @@ def create_image(product):
     print(string)
     img = Image.new('RGB', (560, 112), color='white')
     d = ImageDraw.Draw(img)
-    fnt = ImageFont.truetype('D:\Studia\Projects\AL-2020\img\codes\\arial_bold.ttf', 75)
+    fnt = ImageFont.truetype('img\codes\\arial_bold.ttf', 75)
-    d.text((30, 30), string, font=fnt, fill=(0, 0, 0))
+    d.text((28, 28), string, font=fnt, fill=(0, 0, 0))
     path = 'img/codes/' + product.name + '.png'
     img.save(path)
     return path

coder/rocognizer.py

@@ -1,56 +0,0 @@
-import cv2
-import torch
-from nn_model import Net
-from torchvision.transforms import transforms
-
-
-def recognizer(paths):
-
-    codes = []
-    code = []
-
-    transform = transforms.Compose([transforms.ToTensor(),
-                                    transforms.Normalize((0.5,), (0.5,)),
-                                    ])
-
-    # load nn model
-    model = Net()
-    model.load_state_dict(torch.load('model.pt'))
-    model.eval()
-
-
-    for path in paths:
-        img = cv2.imread(path)
-
-        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)
-
-        ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV)
-
-        ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        rects = [cv2.boundingRect(ctr) for ctr in ctrs]
-
-        for rect in rects:
-            # Crop image
-            crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
-            # Resize the image
-            roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC)
-            # roi = cv2.dilate(roi, (3, 3))
-            # plt.imshow(roi)
-            # plt.show()
-            im = transform(roi)
-            im = im.view(1, 1, 28, 28)
-            with torch.no_grad():
-                logps = model(im)
-            ps = torch.exp(logps)
-            probab = list(ps.numpy()[0])
-            code.append(probab.index(max(probab)))
-
-        codes.append(code)
-        # cv2.imshow("Code", img)
-        # cv2.waitKey()
-
-    return codes
-
-

codes_recognizer/nn_model.py

@@ -18,4 +18,4 @@ class Net(nn.Module):
         x = F.relu(self.fc1(x))
         x = F.dropout(x, training=self.training)
         x = self.fc2(x)
-        return F.log_softmax(x)
+        return F.log_softmax(x, 1)

codes_recognizer/rocognizer.py

@@ -0,0 +1,52 @@
+import cv2
+import torch
+from nn_model import Net
+from torchvision.transforms import transforms
+
+
+def recognizer(path):
+    codes = []
+    code = []
+
+    transform = transforms.Compose([transforms.ToTensor(),
+                                    transforms.Normalize((0.5,), (0.5,)),
+                                    ])
+
+    # load nn model
+    model = Net()
+    model.load_state_dict(torch.load('model.pt'))
+    model.eval()
+
+    img = cv2.imread(path)
+
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)
+
+    ret, im_th = cv2.threshold(img_gray, 90, 255, cv2.THRESH_BINARY_INV)
+
+    ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    rects = [cv2.boundingRect(ctr) for ctr in ctrs]
+
+    for rect in rects:
+        # Crop image
+        crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
+        # Resize the image
+        roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC)
+        # roi = cv2.dilate(roi, (3, 3))
+        # plt.imshow(roi)
+        # plt.show()
+        im = transform(roi)
+        im = im.view(1, 1, 28, 28)
+        with torch.no_grad():
+            logps = model(im)
+        ps = torch.exp(logps)
+        probab = list(ps.numpy()[0])
+        code.append(probab.index(max(probab)))
+
+    codes.append(code)
+    # cv2.imshow("Code", img)
+    # cv2.waitKey()
+
+    return codes
+

main.py

@@ -7,7 +7,7 @@ import data
 
 from agent import Agent
 from settings import Settings
-from board import create_board, draw_board
+from board import create_board, draw_board, get_shelfs
 from random import randint, choice
 from mcda import selectedSupply
 from product import FinalProduct
@@ -95,4 +95,5 @@ def run():
         pygame.display.flip()
 
 
+
 run()

shelf.py

@@ -3,9 +3,24 @@ import pygame
 
 class Shelf:
 
-    def __init__(self, id):
+    def __init__(self, x, y):
-        self.id = id
+        self.x = x
+        self.y = y
         self.products = []
 
-    def addProduct(self, sweet):
+    def add_product(self, product):
-        self.products.append(sweet)
+        self.products.append(product)
+
+    def get_product(self):
+        return self.products[0]
+
+    def get_coordinates(self):
+        c = [self.x, self.y]
+        return c
+
+    def is_empty(self):
+        if len(self.products) == 0:
+            return True
+        else:
+            return False
+