This is a brief overview and introduction to the YOLO object detection algorithm.

So this is one of the state of the art image detection algorithms, and it's known as YOLO, which stands for you only look once YOLO can view an image and then draw bounding boxes over what it perceives as identified classes.

Welcome!

You will need to download this large weights file (over 200MB):
yalo.v5

CODE SOURCE: https://github.com/xiaochus/YOLOv3
Quick start

/YOLO/data/

@article{YOLOv3,  
          title={YOLOv3: An Incremental Improvement},  
          author={J Redmon, A Farhadi },
          year={2018}

!pip3 install opencv-python

!pip3 install tensorflow

!pip3 install keras

import os
import time
import cv2
import numpy as np
from model.yolo_model import YOLO

def process_image(img):
  image = cv2.resize(img, (416, 416),interpolation=cv2.INTER_CUBIC)
  image = np.array(image, dtype='float32')
  image /= 255.
  image = np.expand_dims(image, axis=0)

  return image

def get_classes(file):
with open(file) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]

return class_names

def draw(image, boxes, scores, classes, all_classes):
  for box, score, cl in zip(boxes, scores, classes):
    x, y, w, h = box
    top = max(0, np.floor(x + 0.5).astype(int))
    left = max(0, np.floor(y + 0.5).astype(int))
    right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
    bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
    cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 
      2)
    cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score),  
      (top, left - 6),cv2.FONT_HERSHEY_SIMPLEX,0.6, (0, 0, 255), 
      1,cv2.LINE_AA)
  
    print('class: {0}, score: {1:.2f}'.format(all_classes[cl], 
       score))
    print('box coordinate x,y,w,h: {0}'.format(box))

print()

def detect_image(image, yolo, all_classes):

  pimage = process_image(image)
  start = time.time()
  boxes, classes, scores = yolo.predict(pimage, image.shape)
  end = time.time()

  print('time: {0:.2f}s'.format(end - start))

  if boxes is not None:
    draw(image, boxes, scores, classes, all_classes)

  return image

def detect_video(video, yolo, all_classes):
  video_path = os.path.join("videos", "test", video)
  camera = cv2.VideoCapture(video_path)
  cv2.namedWindow("detection", cv2.WINDOW_AUTOSIZE)

  # Prepare for saving the detected video
  sz = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)),
      int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT)))

  fourcc = cv2.VideoWriter_fourcc(*'mpeg')
  vout = cv2.VideoWriter()
  vout.open(os.path.join("videos", "res", video), fourcc, 20, sz, 
    True)

while True:
  res, frame = camera.read()
  if not res:
    break

    image = detect_image(frame, yolo, all_classes)
    cv2.imshow("detection", image)
    # Save the video frame by frame
    vout.write(image)
  if cv2.waitKey(110) & 0xff == 27:
    break

vout.release()
camera.release()

yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)

WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.

f = 'spoon_knife.jpg'
# f = 'carrot_apple.jpg'
# f = 'bus_traffic_light.jpg'
# f = 'central_market.jpg'
# f = 'friend_groups.jpg'
# f = 'fruit_basket.jpg'
# f = 'house_kitchen.jpg'
# f = 'spoon_knife.jpg'
# f = 'tv_laptop.jpg'
# f = 'vegetable.jpg'
# f = 'woman_on_track.jpg'
path = 'images/test/'+f
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)

1/1 [==============================] - 1s 1s/step
time: 1.33s
class: fork, score: 0.92
box coordinate x,y,w,h: [187.73759604  19.22580004  36.17203832 347.9186058 ]
class: knife, score: 0.87
box coordinate x,y,w,h: [230.02996445  27.17533112  26.21091604 358.97622108]
class: knife, score: 0.77
box coordinate x,y,w,h: [240.34616947   1.77932978  25.62791705 385.19451618]
class: spoon, score: 0.84
box coordinate x,y,w,h: [124.01204109 116.89527035  53.72431278 232.19351768]

True

print("That's it! Thank you once again!\nI hope will be helpful.")

That's it! Thank you once again!
I hope will be helpful.

Note that not all objects were detected.
This technique is evolving!

I think we got the message across.

To run on your win 10 platform download all the files.
Then go to that site and download yalo.v5 and upload it to the directory as shown on this image.