def save_image_and_objects(self, image, image_name, image_ext, objects):
""" Saves the image to a folder of results with a unique file name
timestamp prevents the files from being over-written
image nameand checkpoint name help understand what processing was done
to get the results"""
timestamp = str(datetime.datetime.now()).replace(':', '_').replace(' ', '_')
image_output_name = image_name.replace("." + image_ext, "_" + self.checkpoint + "_" +
timestamp + "." + image_ext)
# If directory does not exist create it
if not os.path.isdir('.\\results'):
os.mkdir('.\\results', mode=0o777)
vis_objects(image, objects).save(os.path.join('.\\results', image_output_name))
def detect(self, image_path, detector):
image = read_image(image_path)
#image = read_image('C:\\Users\\Klaymen-Island\\PycharmProjects\\TabExImg\\src\\data\\02_preprocessed_images\\eu-009\\eu-009_1.jpg')
# If no checkpoint specified, will assume `accurate` by default. In this case,
# we want to use our traffic checkpoint. The Detector can also take a config
# object.
# Returns a dictionary with the detections.
objects = detector.predict(image)
vis_objects(image, objects).save('traffic-out.png')
return objects
def predict_anchor(
image_path="/Users/balajidr/Developer/fyp_final/mainapp/functions/RCNN/testimages/slide-Table.jpg"
):
image = read_image(image_path)
# If no checkpoint specified, will assume `accurate` by default. In this case,
# we want to use our traffic checkpoint. The Detector can also take a config
# object.
# Returns a dictionary with the detections.
objects = detector.predict(image)
print(objects)
vis_objects(image, objects).save('traffic-out.png')
return objects
def predictor_object(
checkpoint,
image,
save_image=False,
):
prediction_objects = Detector(checkpoint=checkpoint).predict(
read_image(image))
image_output_path = str(Path.cwd() / 'images_output' /
'output-{date}.png').format(date=date.today())
if save_image:
vis_objects(
read_image(image),
prediction_objects,
).save(image_output_path)
return prediction_objects
return prediction_objects
def run_module():
"""Runs Luminoth module to simulate perception outputs.
Args:
images: String or list of strings for the paths or directories to
run predictions in. For directories, will return all the files
within.
Returns:
List of objects detected (bounding box with probabaility and name of object.
"""
images = [
cv2.imread(file)
for file in glob.glob(r'E:\CarTests\*.' + 'IMAGE_FORMATS')
]
detector = Detector(checkpoint='cars')
# Returns a dictionary with the detections.
objects = detector.predict(images)
print(objects)
vis_objects(images, objects).save(r'E:\CarTests\objects')
return objects
def visual_objects(image, objects):
vis_objects(image, objects).save(BaseConfig.IMAGE_OUTPUT)
def run(self, save_path):
detector = Detector(self.checkpoint)
image = read_image(self.image)
objects = detector.predict(image)
vis_objects(image, objects).save(save_path)
return objects
from luminoth import Detector, read_image, vis_objects
from PIL import Image
import os
# Changing the current directory in the one of the .py file
try:
os.chdir(os.path.dirname(__file__))
except:
pass
# Reading the .jpg image
image = read_image('Pets.jpg')
# Creating the detector
detector = Detector()
# Returning a dictionary with the detections
objects = detector.predict(image)
print(objects)
# Creating a .jpg file with the detections
vis_objects(image, objects).save('Pets-out.jpg')
# Showing the image
image = Image.open('Pets-out.jpg')
image.show()
# Deleting the image
file_path = 'Pets-out.jpg'
os.remove(file_path)