karthick965938

import cv2
# Load the cascade
#https://github.com/karthick965938/Face-detection/blob/master/haarcascades/haarcascade_frontalface_default.xml
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
# To capture video from webcam. 
cap = cv2.VideoCapture(0)
# To use a video file as input
while True:
    # Read the frame
    _, img = cap.read()

karthick965938

import os
os.getcwd()
collection = "images/cat"
for i, filename in enumerate(os.listdir(collection)):
  print(filename)
  os.rename(collection + "/" + filename, collection + "/cat" + str(i) + ".jpg")

karthick965938

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)

karthick965938

import glob
import pathlib

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

karthick965938

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)

karthick965938

#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()

karthick965938

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

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)

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

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

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