from imageai.Detection import ObjectDetection
import os
from time import time
execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5"))
detector.loadModel(detection_speed="flash")
our_time = time()
detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , "6.jpg"), output_image_path=os.path.join(execution_path , "6flash.jpg"), minimum_percentage_probability=30)
print("IT TOOK : ", time() - our_time)
for eachObject in detections:
print(eachObject["name"] + " : " + eachObject["percentage_probability"] )
print("--------------------------------")
class ObjectPretrainedDetector:
__recognized_classes = {'airplane': 'valid',
'apple': 'invalid',
'backpack': 'invalid',
'banana': 'invalid',
'baseball_bat': 'invalid',
'baseball_glove': 'invalid',
'bear': 'invalid',
'bed': 'invalid',
'bench': 'invalid',
'bicycle': 'invalid',
'bird': 'invalid',
'boat': 'invalid',
'book': 'invalid',
'bottle': 'invalid',
'bowl': 'invalid',
'broccoli': 'invalid',
'bus': 'invalid',
'cake': 'invalid',
'car': 'invalid',
'carrot': 'invalid',
'cat': 'invalid',
'cell_phone': 'invalid',
'chair': 'invalid',
'clock': 'invalid',
'couch': 'invalid',
'cow': 'invalid',
'cup': 'invalid',
'dining_table': 'invalid',
'dog': 'invalid',
'donut': 'invalid',
'elephant': 'invalid',
'fire_hydrant': 'invalid',
'fork': 'invalid',
'frisbee': 'invalid',
'giraffe': 'invalid',
'hair_dryer': 'invalid',
'handbag': 'invalid',
'horse': 'invalid',
'hot_dog': 'invalid',
'keyboard': 'invalid',
'kite': 'invalid',
'knife': 'invalid',
'laptop': 'invalid',
'microwave': 'invalid',
'motorcycle': 'invalid',
'mouse': 'invalid',
'orange': 'invalid',
'oven': 'invalid',
'parking_meter': 'invalid',
'person': 'invalid',
'pizza': 'invalid',
'potted_plant': 'invalid',
'refrigerator': 'invalid',
'remote': 'invalid',
'sandwich': 'invalid',
'scissors': 'invalid',
'sheep': 'invalid',
'sink': 'invalid',
'skateboard': 'invalid',
'skis': 'invalid',
'snowboard': 'invalid',
'spoon': 'invalid',
'sports_ball': 'invalid',
'stop_sign': 'invalid',
'suitcase': 'invalid',
'surfboard': 'invalid',
'teddy_bear': 'invalid',
'tennis_racket': 'invalid',
'tie': 'invalid',
'toaster': 'invalid',
'toilet': 'invalid',
'toothbrush': 'invalid',
'traffic_light': 'invalid',
'train': 'invalid',
'truck': 'invalid',
'tv': 'invalid',
'umbrella': 'invalid',
'vase': 'invalid',
'wine_glass': 'invalid',
'zebra': 'invalid'
}
def __init__(self, source_dir, output_dir, pretrained_model_name=None, search_for_classes=None,
min_proba_to_include=0.3):
self._source_dir = source_dir
self._output_dir = output_dir
self._pretrained_model_name = pretrained_model_name
self._search_for_classes = search_for_classes
self._min_proba_to_include = min_proba_to_include * 100
self._custom_objects = None
self._detector = ObjectDetection()
try:
if pretrained_model_name:
if pretrained_model_name == 'retina':
self._detector.setModelTypeAsRetinaNet()
self._detector.setModelPath('./models/coco_model.h5') # ... retina name
elif pretrained_model_name == 'yolo':
self._detector.setModelTypeAsYOLOv3()
self._detector.setModelPath('./models/yolo.h5') # ... yolo name
elif pretrained_model_name == 'yolo_small':
self._detector.setModelTypeAsTinyYOLOv3()
self._detector.setModelPath('./models/yolo-tiny.h5') # ... yolo small
else:
self._detector.setModelTypeAsTinyYOLOv3()
self._detector.setModelPath('./models/yolo-tiny.h5') # ... yolo small
except FileNotFoundError as e:
raise FileNotFoundError("The model name you provided is not loaded in ./models folder")
self._detector.loadModel()
if search_for_classes:
self._custom_objects = self._detector.CustomObjects(
**{k: (True if k in search_for_classes else False) for k in
ObjectPretrainedDetector.__recognized_classes})
print('Model loaded!')
def launch(self):
imgs = [os.path.join(self._source_dir, x) for x in os.listdir(self._source_dir)]
for idx, img in enumerate(imgs):
if self._custom_objects:
_ = self._detector.detectCustomObjectsFromImage(custom_objects=self._custom_objects,
input_image=img,
output_image_path=f'{self._output_dir}/{idx}',
minimum_percentage_probability=self._min_proba_to_include)
else:
_ = self._detector.detectObjectsFromImage(input_image=img,
output_image_path=f'{self._output_dir}/{idx}',
minimum_percentage_probability=self._min_proba_to_include)
from imageai.Detection import ObjectDetection
import os
execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5"))
detector.loadModel()
detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , "download.jpg"), output_image_path=os.path.join(execution_path , "imagenew.jpg"))
for eachObject in detections:
print(eachObject["name"] + " : " + eachObject["percentage_probability"] )
detections, extracted_images = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , "image.jpg"), output_image_path=os.path.join(execution_path , "imagenew.jpg"), extract_detected_objects=True)
'''
Editor: Ahan M R
Date: 18-06-2018
Python 3.6.0
'''
#PS1 CROWD DETECTION IN THE SITUATION
'''
From imageAI API, we import Object detection and use it to detect the object classes in the image
'''
from imageai.Detection import ObjectDetection
import os
#os.getcwd gets the current working directory of the image
execution_path = os.getcwd()
detection = ObjectDetection()
detection.setModelTypeAsRetinaNet()
detection.setModelPath(
os.path.join(execution_path, "resnet50_coco_best_v2.0.1.h5"))
detection.loadModel()
detections = detection.detectObjectsFromImage(
input_image=os.path.join(execution_path, "image.jpg"),
output_image_path=os.path.join(execution_path, "imagenew.jpg"))
for eachObject in detections:
print(eachObject["name"] + " : " + eachObject["percentage_probability"])
