forked from s444399/AI
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
import numpy
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import cv2
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import argparse
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import numpy as np
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class Neurons:
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def __init__(self):
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pass
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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 whatIsIt(self, path):
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image = cv2.imread(path)
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scale = 0.00392
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classes = None
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with open("yolov3.txt", 'r') as f:
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classes = [line.strip() for line in f.readlines()]
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net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
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blob = cv2.dnn.blobFromImage(image, scale, (608, 608), (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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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 > 0.2:
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class_ids.append(class_id)
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y = [classes[ids] for ids in class_ids]
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print(y)
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x = [0, 0, 0, 0, 0]
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if "kruche" in y:
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x[1]=1
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elif "niebezpieczne" in y:
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x[4]=1
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elif "biohazard" in y:
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x[4]=1
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elif "radioaktywne" in y:
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x[3]=1
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elif "latwopalne" in y:
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x[2]=1
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print(x)
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if x[0]==1:
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print("Zwykła")
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if x[1]==1:
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print("Kruchy")
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if x[2]==1:
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print("Łatwopalny")
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if x[3]==1:
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print("Radioaktywny")
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if x[4]==1:
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print("Niebezpieczny")
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return [list(x)]
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