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!
 

1. What is a Mask-RCNN model?

A Mask-RCNN (Mask Region-based Convolutional Neural Network) model is a deep learning model that is used for object detection and instance segmentation. It combines the concepts of Faster R-CNN (Region-based Convolutional Neural Network) and Mask R-CNN to accurately detect and classify objects in an image while also generating pixel-level masks for each object.

2. How does a Mask-RCNN model work?

A Mask-RCNN model works by first using a Region Proposal Network (RPN) to generate potential object bounding boxes in an image. Then, a feature map is extracted from the image and used to classify each proposed region as either a foreground object or background. Finally, the model generates pixel-level masks for each object by using a convolutional neural network to refine the initial bounding box and classify each pixel within the box as belonging to a specific object or background.

3. What are the advantages of using a Mask-RCNN model for smart parking systems?

A Mask-RCNN model offers several advantages for smart parking systems, including accurate detection and classification of vehicles, generation of pixel-level masks for precise object segmentation, and the ability to handle occlusions and overlapping objects. Additionally, the model can be trained to recognize specific types of vehicles, such as cars, trucks, or motorcycles, which can be useful for managing parking spaces and optimizing parking availability.

4. How can I create a Mask-RCNN model in Python?

To create a Mask-RCNN model in Python, you can use a deep learning framework such as TensorFlow or PyTorch. There are also pre-trained Mask-RCNN models available that can be fine-tuned for your specific application. It is recommended to have a good understanding of deep learning and computer vision concepts before attempting to create a Mask-RCNN model.

5. What are some potential challenges when implementing a Mask-RCNN model for a smart parking system?

One potential challenge when implementing a Mask-RCNN model for a smart parking system is ensuring that the model is trained on a diverse dataset that includes different types of vehicles, lighting conditions, and backgrounds. Additionally, the model may struggle with occlusions or overlapping vehicles, so it is important to continually monitor and update the model's performance. Another challenge could be the computational resources required to run the model, as Mask-RCNN models can be quite complex and may require a powerful GPU for efficient training and inference.

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