<jupyter_start><jupyter_text>The Flood AIML Model to Predict Houses  LibrariesStock-standard libararies for any machine learning project in Python.<jupyter_code>import tensorflow as tf
import numpy as np
import seaborn as sns
import pandas as pd
import keras
import imageai
from imageai.Detection import ObjectDetection
detector = ObjectDetection()
model_path = "./models/Model.h5"

# Example: Traffic (working).
# input_path = "./input/Traffic.jpeg"
# output_path = "./output/Traffic.jpeg"

# Example: Satellite (not working!)
input_path = "./input/Satellite.png"
output_path = "./output/Satellite.png"
detector.setModelTypeAsTinyYOLOv3()
detector.setModelPath(model_path)
detector.loadModel()
detection = detector.detectObjectsFromImage(input_image=input_path, output_image_path=output_path)

for eachItem in detection:
    print(eachItem["name"] , " : ", eachItem["percentage_probability"])
from skimage import data
import numpy as np
import matplotlib.pyplot as plt
image = data.binary_blobs()
plt.imshow(image, cmap='gray')
from skimage import data
import numpy as np
import matplotlib.pyplot as plt
from skimage import io

input_path = "./input/Satellite.png"
image = io.imread(input_path)
plt.imshow(image)
def image_show(image, nrows=1, ncols=1, cmap='gray'):
    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))
    ax.imshow(image, cmap='gray')
    ax.axis('off')
    return fig, ax

# image_show(image)
import numpy as np
import matplotlib.pyplot as plt
import skimage.data as data
import skimage.segmentation as seg
import skimage.filters as filters
import skimage.draw as draw
import skimage.color as color

image_gray = color.rgb2gray(image) 
image_show(image_gray);<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '73043', 'license': 'MIT', 'n_tokens_mistral': 587, 'n_tokens_neox': 570, 'n_words': 156, 'path': 'notebook/Main.ipynb', 'repo_name': 'woodRock/the-flood', 'size': '1728'}