from utils.elpv_reader import load_dataset
import cv2
import numpy as np
import shutil
import os
from sklearn.model_selection import train_test_split
destination_folder = 'solar_panels_products_V2'
if not os.path.exists(destination_folder):
os.mkdir(destination_folder)
# datafolder is the relative path for the contents in the elpv dataset
images, probs, types = load_dataset(datafolder='elpv_dataset')
train_images, test_images, train_probs, test_probs, train_types, test_types = train_test_split(
images, probs, types, test_size=0.1)
def create_dataset(images, probs, types, split):
threshold = 0.6
data_folder = os.path.join(destination_folder, split)
if not os.path.exists(data_folder):
os.mkdir(data_folder)
idx = 0
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
# In[2]:
from utils.elpv_reader import load_dataset
images, proba, types = load_dataset()
# In[3]:
import matplotlib.pyplot as plt
from tensorflow.keras import datasets, layers, models
height = 200
# In[4]:
images = images / 255.
images = images.reshape(2624, height, height, 1)
# In[5]:
model = models.Sequential()
model.add(
layers.Conv2D(64, (7, 7),