Let’s get started with the code:
# Load an image for object detection using cv2
image = cv2.imread(‘path/to/image.jpg’)
# Define rules based on image properties
# Returns True if image contains a person, otherwise returns False
# Use a pre-trained person detection model, e.g.
YOLOv3 , to detect people in the image
The predefined YOLO model and weights are open source and can be downloaded at https://pjreddie.com/darknet/yolo:
def has_person(image):
# Load the YOLOv3 model with its weights and configuration files
net = cv2.dnn.readNetFromDarknet(“path/to/yolov3.cfg”, \
“path/to/yolov3.weights”)
# Load the COCO class names (used for labeling detected objects)
classes = []
with open(“path/to/coco.names”, “r”) as f:
classes = [line.strip() for line in f.readlines()]
# Create a blob from the image and set it as input to the network
blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), \
swapRB=True, crop=False) net.setInput(blob)
# Run forward pass to perform object detection
detections = net.forward()
# Process and interpret the detection results
for detection in detections:
# Process detection results and draw bounding boxes if needed
# You can use classes to map class IDs to class names
if confidence > confidence_threshold and classes[class_id] == “person”:
if len(boxes) > 0:
return True
else:
return False
In this code, we use OpenCV to load the YOLO V3 model, its weights, and its configuration files. Then, we provide an input image, run a forward pass through the network, and process the detection results.
You’ll need to replace “path/to/yolov3.cfg”, “path/to/coco.names”, and “path/to/image.jpg” with the actual paths to your YOLOv3 configuration file, the class names file, and the image that you want to perform object detection on.
Remember that YOLO V3 is a complex deep learning model designed for real-time object detection, and using it effectively often requires some knowledge of computer vision and deep learning concepts.
Example – bicycle image detection using the YOLO V3 pre-trained classifier
The following is the code for this example:
def has_bicycle(image):
# Returns True if image contains a bicycle, otherwise returns False
model = tf.saved_model.load(
“path/to/faster_rcnn_inception_v2_coco_2018_01_28/saved_model”)
img_resized = cv2.resize(image, (600, 600))
input_tensor = tf.convert_to_tensor(img_resized)
input_tensor = input_tensor[tf.newaxis, …]
detections = model(input_tensor)
num_detections = int(detections.pop(‘num_detections’))
detections = {key: value[0, :num_detections].numpy() \
for key, value in detections.items()}
In summary, the code snippet utilizes a pre-trained Faster R-CNN model to perform object detection on an input image. It resizes the image, converts it to a tensor, and then extracts and processes the detection results. To specifically detect bicycles, you would need to filter the results based on the class labels provided by the model and check for the presence of bicycles in the detected objects.
Now, let us explore how we can apply transformations on a given image dataset to generate additional synthetic data. Additional synthetic data helps in training and achieving more accurate results, as a model will learn about different positions of images.