help="path to input image")
args = vars(ap.parse_args())
# load the anomaly detection model
print("[INFO] loading anomaly detection model...")
model = pickle.loads(open(args["model"], "rb").read())
histo = RGBHistogram(bins=(3, 3, 3),
include_color_stats=True,
color_cvt=cv2.COLOR_BGR2HSV)
imagePaths = list_images(args["dataset_root"])
image_count = 0
correct_count = 0
for imagePath in imagePaths:
image_count += 1
features, image = histo.get_features(imagePath)
pred = model.predict([features])[0]
if 'forest' in imagePath:
if pred == 1:
correct_count += 1
else:
if pred == -1:
correct_count += 1
print(
f"Anomly Detection Accuracy: {correct_count/image_count} from a total image dataset of {image_count} images"
)
if __name__ == '__main__':
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m",
"--model",
required=False,
default="./models/forest_anomoly_detector.model",
help="path to trained anomaly detection model")
ap.add_argument("-i", "--image", required=True, help="path to input image")
args = vars(ap.parse_args())
# load the anomaly detection model
print("[INFO] loading anomaly detection model...")
model = pickle.loads(open(args["model"], "rb").read())
histo = RGBHistogram(bins=(3, 3, 3),
include_color_stats=True,
color_cvt=cv2.COLOR_BGR2HSV)
features, image = histo.get_features(args['image'])
pred = model.predict([features])[0]
label = 'anomly' if pred == -1 else 'normal'
color = (0, 0, 255) if pred == -1 else (0, 255, 0)
cv2.putText(image, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color,
2)
cv2.imshow("Detector", image)
cv2.waitKey(0)