Gotowy podprojekt

Marcin.py

@@ -1,5 +1,7 @@
-# import keras
-import numpy
+import numpy as np
+import cv2
+import pygame
+
 
 class main():
     def __init__(self,traktor,field,ui,path):
@@ -8,5 +10,62 @@ class main():
         self.ui = ui
         self.path = path
 
+    def get_output_layers(self,net):
+        layer_names = net.getLayerNames()
+        output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+        return output_layers
+
+    def recognition(self,photo):
+        image = photo
+
+        Width = image.shape[1]
+        Height = image.shape[0]
+        scale = 0.00392
+
+        with open("si.names", 'r') as f:
+            classes = [line.strip() for line in f.readlines()]
+
+        COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+
+        net = cv2.dnn.readNet("si_final.weights", "si.cfg")
+
+        blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
+
+        net.setInput(blob)
+
+        outs = net.forward(self.get_output_layers(net))
+
+        class_ids = []
+        confidences = []
+        boxes = []
+        conf_threshold = 0.5
+        nms_threshold = 0.4
+
+        for out in outs:
+            for detection in out:
+                scores = detection[5:]
+                class_id = np.argmax(scores)
+                confidence = scores[class_id]
+                if confidence > 0.5:
+                    class_ids.append(class_id)
+        print(class_id)
+        print(scores)
+        return class_id
+
     def main(self):
-        pass
+        self.pole = self.ui.field_images[self.field.get_value(self.traktor.get_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.traktor.set_mode(self.mode(self.recognition(self.img)))
+
+    def mode(self,mode):
+        self.mode_value = mode
+        if self.mode_value in [0, 1, 2, 3]:
+            return 0
+        elif self.mode_value in [1, 3, 5, 7]:
+            return 1
+        elif self.mode_value in [0, 1, 4, 5]:
+            return 2
+        elif self.mode_value in [8]:
+            return 3