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from detecto import core, utils, visualize
# Load you image
image = utils.read_image('cat.jpg')
model = core.Model.load('cat_model_weights', ['cat'])
labels, boxes, scores = model.predict_top(image)
##Test your Image
visualize.show_labeled_image(image, boxes, labels)
##Test your Video
# visualize.detect_video(model, 'cat.mp4', 'output.avi')
##Live Detection

karthick965938

from detecto import core, utils, visualize
image = utils.read_image('apple.jpg')
#Initialized a pre-trained model
model = core.Model() 
labels, boxes, scores = model.predict_top(image)
visualize.show_labeled_image(image, boxes, labels)

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model.save('model_weights.pth')
#you can save your model using above file

model = core.Model.load('model_weights.pth', ['alien', 'bat', 'witch'])

karthick965938

# Specify the path to your image
from detecto import core, utils, visualize
image = utils.read_image('animals/cat48.jpg')
predictions = model.predict(image)
# predictions format: (labels, boxes, scores)
labels, boxes, scores = predictions
# ['alien', 'bat', 'bat']
print(labels) 
print(boxes)
# tensor([0.9952, 0.9837, 0.5153])

karthick965938

from detecto import core, utils, visualize
#mention you dataset path
dataset = core.Dataset('images/')
#mention you object label here
model = core.Model(['cat'])
model.fit(dataset)

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import os
from google.colab import drive
drive.mount('/content/drive')
os.chdir('/content/drive/My Drive/object_detection')
!pip install detecto

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#Get images from the google
#Remove PNG images
import glob
import pathlib
for file in glob.glob('frames/*.png'):
  path = pathlib.Path(file)
  path.unlink()
#Rename your images files form the image directory
import os
os.getcwd()

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from PIL import Image
import os
import argparse

def rescale_images(directory, size):
  for img in os.listdir(directory):
    im = Image.open(directory+img)
    im_resized = im.resize(size, Image.ANTIALIAS)
    im_resized.save(directory+img)

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import glob
import pathlib

for file in glob.glob('frames/*.png'):
  path = pathlib.Path(file)
  path.unlink()

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from detecto.utils import split_video
#split_video('video file path', 'image save path', frame size)
split_video('images/cat.mp4', 'images/cat/', step_size=10)