def search():
btn['state'] = NORMAL #open image button is returened to enabled
fracturedataset = core.Dataset(
'trainingimagesgui/'
) #initializes the dataset the parameter is the folder in which to get the images for the dataset
mymodel = core.Model([
'crack'
]) #calls the neural network and it is looking for boxes labelled crack
print("model imported"
) #print function to see if the neural network has been called
mymodel.fit(fracturedataset) #train the neuralnetwork on the dataset
print("finished training custom dataset"
) #prints to show that the neural network has finished training
#testing the neural network
testimage = utils.read_image(
filename
) #read the varible testimage which has the location of what is stored in variable filename
print(filename) #test to see the correct location is being pulled
endresult = mymodel.predict(
testimage) #run the test image on the now trained neural network
print(
"prediction complete"
) #test to see that the test image has successfully ran on the neural network
labels, boxes, scores = endresult #all the parameters the test image will have
print(labels) #test to see if the correct label is printed
print(boxes) #this will print the coordiantes of the boxes
print(scores) #prints a score of how confident the neural network is
visualize.show_labeled_image(testimage, boxes,
labels) #display the image on the screen
def classificate(self, image_path):
image = detecto_utils.read_image(image_path)
labels, boxes, scores = self.detector.predict(image)
if len(boxes) == 0:
return False
if DEBUG:
detecto_visualize.show_labeled_image(image, boxes, labels)
labels_numpy = boxes.detach().cpu().numpy()
for box in boxes:
detected_logo = image[int(box[1]):int(box[3]),
int(box[0]):int(box[2]), :]
if self.classifier.classificate(detected_logo):
return True
return False
def predict_one(self):
# Specify the path to your image
print(self.imagePath)
image = utils.read_image(self.imagePath)
predictions = self.model.predict(image)
# predictions format: (labels, boxes, scores)
labels, boxes, scores = predictions
# ['alien', 'bat', 'bat']
print(labels)
# xmin ymin xmax ymax
# tensor([[ 569.2125, 203.6702, 1003.4383, 658.1044],
# [ 276.2478, 144.0074, 579.6044, 508.7444],
# [ 277.2929, 162.6719, 627.9399, 511.9841]])
print(boxes)
# tensor([0.9952, 0.9837, 0.5153])
print(scores)
visualize.show_labeled_image(image, boxes, labels)
from detecto import core, utils, visualize
image = utils.read_image('cat.jpg')
model = core.Model()
labels, boxes, scores = model.predict_top(image)
visualize.show_labeled_image(image, boxes, labels)
sample_image = "s1.jpg"
#image = detecto.utils(base_path+sample_image)
# image = cv2.imread(base_path+sample_image)
# cv2.imshow('img',image)
# cv2.waitKey(0)
img = cv2.imread(base_path+sample_image)
# image = read_image(base_path+sample_image)
# plt.imshow(image)
# plt.show()
# cv2.imshow('img',img)
# cv2.waitKey(0)
dataset = Dataset(base_path)
img, targets = dataset[10]
show_labeled_image(img, targets['boxes'], targets['labels'])
labels = ['spiderman', 'venom']
model = Model(labels)
model.fit(dataset)
torch.save(model, 'model.pth')
# directory = r'C:\Users\kapsi\Desktop\WdPO_P\test'
# file_count = sum(len(files) for _, _, files in os.walk(directory))
# if file_count == 0:
# print('Theres nothing to show! Closing...')
#
# else:
# pics = list()
# for i in range(file_count):
# path = test_data + '{}.jpg'.format(i)
def run_detect(image_name):
image = utils.read_image('static/' + image_name)
model = core.Model()
labels, boxes, scores = model.predict_top(image)
return visualize.show_labeled_image(image, boxes, labels)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
if (cap.isOpened() == False):
print("Error opening video stream or file")
i = 0
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == True:
cv2.imshow('Video', frame)
predictions = model.predict(frame)
labels, boxes, scores = predictions
if len(scores) > 0 and max(scores) > 0.9:
visualize.show_labeled_image(frame, boxes[0], labels[0])
if i == 0:
win32api.MessageBox(0, 'KNIFE DETECTED. SAVING IMAGE.',
'ALERT', 0x00001000)
x1, y1, x2, y2 = boxes[0]
img = cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)),
(0, 255, 0), 3)
cv2.imshow('detected', frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('knifedetected' + str(i) + '.jpg', frame)
sendmail(i)
i = i + 1
# Press Q on keyboard to exit
ch = cv2.waitKey(1)
def detecto_m(pic):
image = utils.read_image(pic)
model = core.Model()
labels, boxes, scores = model.predict_top(image)
result = visualize.show_labeled_image(image, boxes, labels)
return result