#In imageAI we have RetinaNet, YOLOv3 and TinyYOLOv3 for objet detection
from imageai.Detection import ObjectDetection # import the ImageAI object detection class
import os
working_path = os.getcwd()
res = ObjectDetection() #create an object of the ObjectDetection class
res.setModelTypeAsYOLOv3()
# https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo.h5
res.setModulePath(os.path.join(
working_path, "yolo.h5")) #set the model path to the YOLOv3 model file
res.loadModel()
detection = res.detectObjectsFromImage(
input_image=os.path.join(working_path, image_1.jpg),
output_image_path=os.path.join(working_path, image_2.jpg),
minimum_percentage_probability=30)
#Display
for obj in detection:
print(obj["name"], " : ", obj["percentage_probability"], " : ",
obj["box_points"])
# we can also use RetinaNet which is appropriate for high-performance and high-accuracy tasks
# https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5
# res = ObjectDetection()
# res.setModelTypeAsRetinaNet()
# res.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5"))
# res.loadModel()
