86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
import numpy as np
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import cv2
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import pygame
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class main():
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def __init__(self,traktor,field,ui,path):
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self.traktor = traktor
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self.field = field
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self.ui = ui
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self.path = path
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self.mode_value = 0
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def get_output_layers(self,net):
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layer_names = net.getLayerNames()
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output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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return output_layers
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def recognition(self,photo):
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image = photo
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Width = image.shape[1]
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Height = image.shape[0]
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scale = 0.00392
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with open("si.names", 'r') as f:
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classes = [line.strip() for line in f.readlines()]
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COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
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net = cv2.dnn.readNet("si_20000.weights", "si.cfg")
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blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
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net.setInput(blob)
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outs = net.forward(self.get_output_layers(net))
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class_ids = []
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confidences = []
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boxes = []
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conf_threshold = 0.5
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nms_threshold = 0.4
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > 0.5:
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class_ids.append(class_id)
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return class_ids[0]
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def main(self):
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self.pole = self.ui.field_images[self.field.get_value(self.traktor.get_poz())]
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self.img = pygame.surfarray.array3d(self.pole)
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self.img = self.img.transpose([1,0,2])
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self.img = cv2.cvtColor(self.img, cv2.COLOR_RGB2BGR)
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self.reco = self.mode(self.recognition(self.img))
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if self.reco == 10:
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print("Nic nie trzeba robić")
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else:
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self.traktor.set_mode(self.reco)
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def mode(self,mode):
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self.mode_value = mode
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if self.mode_value in [0, 1, 2, 3]:
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return 0
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elif self.mode_value in [1, 3, 5, 7]:
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return 1
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elif self.mode_value in [0, 1, 4, 5]:
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return 2
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elif self.mode_value in [8]:
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return 3
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elif self.mode_value in [6]:
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return 10
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def main_collective(self,poz = None):
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if poz is None:
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poz = self.traktor.get_poz()
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self.pole = self.ui.field_images[self.field.get_value(poz)]
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self.img = pygame.surfarray.array3d(self.pole)
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self.img = self.img.transpose([1, 0, 2])
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self.img = cv2.cvtColor(self.img, cv2.COLOR_RGB2BGR)
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self.reco = self.recognition(self.img)
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return self.reco |