From 39ff697ec75f2b5faf3152ed0ef723fdceb6ec9f Mon Sep 17 00:00:00 2001 From: Michal Kijowski Date: Fri, 8 May 2020 20:36:01 +0000 Subject: [PATCH] Upload files to '' --- neurons.py | 33 ++++++++++++++++++++++----------- 1 file changed, 22 insertions(+), 11 deletions(-) diff --git a/neurons.py b/neurons.py index af6f80f..e7fbf9a 100644 --- a/neurons.py +++ b/neurons.py @@ -11,13 +11,13 @@ class Neurons: output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()] return output_layers def whatIsIt(self, path): - """image = cv2.imread(path) + image = cv2.imread(path) scale = 0.00392 classes = None with open("yolov3.txt", 'r') as f: classes = [line.strip() for line in f.readlines()] net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") - blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False) + blob = cv2.dnn.blobFromImage(image, scale, (608, 608), (0, 0, 0), True, crop=False) net.setInput(blob) outs = net.forward(self.get_output_layers(net)) class_ids = [] @@ -26,19 +26,30 @@ class Neurons: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] - if confidence > 0.5: + if confidence > 0.2: class_ids.append(class_id) - print([classes[ids] for ids in class_ids])""" - x = [1, 0, 0, 0, 0] - numpy.random.shuffle(x) + y = [classes[ids] for ids in class_ids] + print(y) + x = [0, 0, 0, 0, 0] + if "kruche" in y: + x[1]=1 + elif "niebezpieczne" in y: + x[4]=1 + elif "biohazard" in y: + x[4]=1 + elif "radioaktywne" in y: + x[3]=1 + elif "latwopalne" in y: + x[2]=1 + print(x) if x[0]==1: print("Zwykła") - elif x[1]==1: + if x[1]==1: print("Kruchy") - elif x[2]==1: + if x[2]==1: print("Łatwopalny") - elif x[3]==1: + if x[3]==1: print("Radioaktywny") - elif x[4]==1: + if x[4]==1: print("Niebezpieczny") - return [list(x)] \ No newline at end of file + return [list(x)]