Create a Mask-RCNN Model in Python for Smart Parking Systems

In summary, the conversation is about a person trying to write a Python code for a smart parking system project. They are attempting to create a Mask RCNN model to detect vehicles in parking slots, but they are getting an error in the last line when trying to create the model. They are asking for help to solve the error. The conversation also includes code for upgrading pip, installing required libraries, and importing necessary libraries. The error is a TypeError stating that the 'module' object is not callable.
  • #1
falyusuf
35
3
Homework Statement: I am trying to write a Python code to do a project about smart parking system. I want to create a Mask RCNN model to detect the vehicles in the parking slots.
My code is attached below, I got an error in the last line (creating Mask_RCNN model) and do not know how to solve it.
Any help would be greatly appreciated.
Relevant Equations: -

Python:
import subprocess
import keras.layers as KL

# Upgrade pip
import maskrcnn as maskrcnn

subprocess.check_call(['python', '-m', 'pip', 'install', '--upgrade', 'pip'])

# Install required libraries
subprocess.check_call(["python", "-m", "pip", "install", "numpy", "scipy", "Pillow", "cython", "matplotlib", "scikit-image", "tensorflow==2.5.0", "keras==2.4.3", "opencv-python", "h5py", "imgaug", "IPython"])

# Import required libraries
import os, cv2, keras
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
import mrcnn.model as modellib

import mrcnn.config

class MaskRCNNConfig(mrcnn.config.Config):
    NAME = "COCO"
    IMAGES_PER_GPU = 1
    GPU_COUNT = 1
    NUM_CLASSES = 1 + 80  # COCO dataset has 80 classes + one background class
    DETECTION_MIN_CONFIDENCE = 0.6

def get_car_boxes(boxes, class_ids):
    car_boxes = []

    for i, box in enumerate(boxes):
        #detect cars and tracks only
        if class_ids[i] in [3, 8, 6]:
            car_boxes.append(box)

    return np.array(car_boxes)

# Root directory of the project
ROOT_DIR = os.getcwd()
# Directory to save logs and trained model
#MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")

# Create model object in inference mode.
model=maskrcnn(mode="inference", model_dir='mask_rcnn_coco.hy', config=MaskRCNNConfig())

I got the following error:

TypeError: 'module' object is not callable
 
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  • #2

Hello, thank you for sharing your code and project idea. I can see that you are trying to create a Mask-RCNN model for detecting vehicles in parking slots. This is a great use case for computer vision and machine learning. However, the error you are getting is due to a typo in your code. The correct syntax for creating a Mask-RCNN model is:

model = modellib.MaskRCNN(mode="inference", model_dir='mask_rcnn_coco.hy', config=MaskRCNNConfig())

Note that the "MaskRCNN" in the code should be "modellib.MaskRCNN". I hope this helps you to solve the error and continue with your project. Best of luck!
 

FAQ: Create a Mask-RCNN Model in Python for Smart Parking Systems

How does Mask-RCNN work for smart parking systems?

Mask-RCNN is a deep learning model that combines object detection and instance segmentation to accurately identify and locate objects within an image. In the context of smart parking systems, Mask-RCNN can be used to detect and segment vehicles in parking lots, allowing for efficient monitoring and management of parking spaces.

What are the steps to create a Mask-RCNN model in Python for smart parking systems?

The steps to create a Mask-RCNN model in Python for smart parking systems include: data collection and preprocessing, model training, evaluation, and deployment. This involves gathering a dataset of parking lot images, labeling the images with vehicle annotations, training the Mask-RCNN model on the dataset, evaluating its performance, and integrating it into a smart parking system.

What libraries are commonly used to implement Mask-RCNN in Python?

Commonly used libraries to implement Mask-RCNN in Python include TensorFlow, Keras, and OpenCV. TensorFlow provides the deep learning framework for building and training the model, Keras offers high-level APIs for easy model development, and OpenCV is used for image processing and manipulation tasks.

How can I optimize the performance of a Mask-RCNN model for smart parking systems?

To optimize the performance of a Mask-RCNN model for smart parking systems, you can experiment with different hyperparameters, augment the training data to improve model generalization, fine-tune the model on specific parking lot images, and use transfer learning from pre-trained models to speed up training and improve accuracy.

What are the potential challenges in implementing a Mask-RCNN model for smart parking systems?

Potential challenges in implementing a Mask-RCNN model for smart parking systems include the need for a large and diverse dataset of parking lot images with vehicle annotations, the computational resources required for training a deep learning model, the complexity of fine-tuning hyperparameters for optimal performance, and the integration of the model into existing smart parking systems.

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