import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('drive/MyDrive/tree/sample.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gauss = cv2.GaussianBlur(gray, (3,3), 3)

plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.imshow(gauss)

gscale = cv2.Canny(gauss, 80, 140)
plt.subplot(1,2,2)
plt.imshow(gscale)
plt.show()

size = np.size(gscale)

skel = np.zeros(gscale.shape, np.uint8)

ret, gscale = cv2.threshold(gscale, 128, 255,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
done = False

while not done:
  eroded = cv2.erode(gscale, element)
  temp = cv2.dilate(eroded, element)
  temp = cv2.subtract(gscale, temp)
  skel = cv2.bitwise_or(skel, temp)
  gscale = eroded.copy()
  
  zeros = size - cv2.countNonZero(gscale)
  if zeros==size:
    done = True

lines = cv2.HoughLinesP(skel,1,np.pi/180,130)
a,b,c = lines.shape
for i in range(a):
    rho = lines[i][0][0]
    theta = lines[i][0][1]    
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))
    cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2, cv2.LINE_AA)#showing the results:

plt.figure(figsize=(15,15))
plt.subplot(121)#OpenCV reads images as BGR, this corrects so it is displayed as RGB
plt.plot()
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) 
plt.title('Row Detection')
plt.xticks([])
plt.yticks([])
plt.subplot(122)
plt.plot()
plt.imshow(skel,cmap='gray')
plt.title('Skeletal Image')
plt.xticks([])
plt.yticks([])
plt.show()

import numpy as np
from tkinter import Tk,Frame,TOP,BOTH

import matplotlib
from matplotlib import pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

plt.rcParams["figure.figsize"] = [18,10]

root=Tk()
root.wm_title("Root Window")
root.geometry('1500x1000')

x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, ax = plt.subplots()
ax.plot(x, y)

canvas_frame=Frame(root) # also tried adjusting size of frame but that didn't help
canvas_frame.pack(side=TOP,expand=True)
canvas = FigureCanvasTkAgg(fig, master=canvas_frame)
canvas.draw()
canvas.get_tk_widget().pack(side=TOP,fill=BOTH,expand=True)


root.mainloop()

plt.figure(figsize=[21, 6])
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)

ax1.plot(X[:, 0][y == 0], X[:, 1][y == 0], "bo")
ax1.plot(X[:, 0][y == 1], X[:, 1][y == 1], "g^")
ax1.contourf(xx, yy, pred.reshape(xx.shape), cmap=matplotlib.colors.ListedColormap(['b', 'g']), alpha=0.25)
ax1.set_title(title)

plot_tree(tree_clf, feature_names=["X", "y"], class_names=["blue", "green"], filled=True, rounded=True)

import keras
from keras.datasets import cifar10
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPooling2D
from keras.layers.normalization import BatchNormalization

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

#reshape data to fit model
X_train = X_train.reshape(50000,32,32,3)
X_test = X_test.reshape(10000,32,32,3)

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)


# Building the model 

#1st Convolutional Layer
model.add(Conv2D(filters=64, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

#2nd Convolutional Layer
model.add(Conv2D(filters=224, kernel_size=(5, 5), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

#3rd Convolutional Layer
model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))

#4th Convolutional Layer
model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))

#5th Convolutional Layer
model.add(Conv2D(filters=160, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

model.add(Flatten())

# 1st Fully Connected Layer
model.add(Dense(4096, input_shape=(32,32,3,)))
model.add(BatchNormalization())
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))

#2nd Fully Connected Layer
model.add(Dense(4096))
model.add(BatchNormalization())
model.add(Activation('relu'))
#Add Dropout
model.add(Dropout(0.4))

#3rd Fully Connected Layer
model.add(Dense(1000))
model.add(BatchNormalization())
model.add(Activation('relu'))
#Add Dropout
model.add(Dropout(0.4))

#Output Layer
model.add(Dense(10))
model.add(BatchNormalization())
model.add(Activation('softmax'))


#compile model using accuracy to measure model performance
opt = keras.optimizers.Adam(learning_rate = 0.0001)
model.compile(optimizer=opt, loss='categorical_crossentropy', 
              metrics=['accuracy'])


#train the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)

import keras
import scipy
import numpy as np
import matplotlib.pyplot as plt
from keras.utils import to_categorical
from scipy import io

mydict = scipy.io.loadmat('Indian_pines_corrected.mat')
dataset = np.array(mydict.get('indian_pines_corrected'))


#This is creating the split between x_train and x_test from the original dataset 
# x_train after this code runs will have a shape of (121, 145, 200) 
# x_test after this code runs will have a shape of (24, 145, 200)
x_train = np.zeros((121,145,200), dtype=np.int)
x_test = np.zeros((24,145,200), dtype=np.int)    

xtemp = np.array_split(dataset, [121])
x_train = np.array(xtemp[0])
x_test = np.array(xtemp[1])

# x_train will have a shape of (17545, 200) 
# x_test will have a shape of (3480, 200)
x_train = x_train.reshape(-1, x_train.shape[-1])
x_test = x_test.reshape(-1, x_test.shape[-1])

truthDataset = scipy.io.loadmat('Indian_pines_gt.mat')
gTruth = truthDataset.get('indian_pines_gt')

#This is creating the split between Y_train and Y_test from the original dataset 
# Y_train after this code runs will have a shape of (121, 145) 
# Y_test after this code runs will have a shape of (24, 145)

Y_train = np.zeros((121,145), dtype=np.int)
Y_test = np.zeros((24,145), dtype=np.int)    

ytemp = np.array_split(gTruth, [121])
Y_train = np.array(ytemp[0])
Y_test = np.array(ytemp[1])

# Y_train will have a shape of (17545) 
# Y_test will have a shape of (3480)
Y_train = Y_train.reshape(-1)
Y_test = Y_test.reshape(-1)


#17 binary categories ranging from 0-16

#Y_train one-hot encode target column
Y_train = to_categorical(Y_train)

#Y_test one-hot encode target column
Y_test = to_categorical(Y_test, num_classes = 17)

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization

2021-10-06 22:27:14.064885: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-10-06 22:27:14.064974: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "C:\Data\breast-cancer-classification\train_model.py", line 10, in 
    from cancernet.cancernet import CancerNet
  File "C:\Data\breast-cancer-classification\cancernet\cancernet.py", line 2, in 
    from keras.layers.normalization import BatchNormalization
ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\Users\Catalin\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\layers\normalization\__init__.py)

pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

img = cv.imread(r'/Users/harold/Documents/Academia/Nottingham Uni/Year 4/ImageProcessing/Imaging_Task_Sheet/PointImage.jpeg', cv.IMREAD_GRAYSCALE)

img_s = img/255
img_s = img_s / np.max(img_s)
img_s = img_s*255

histogram = cv.calcHist([img], [0], None, [256], [0, 256])
histogram1 = cv.calcHist([img_s.astype('uint8')], [0], None, [256], [0, 256])

plt.figure()
plt.title("Grayscale Histogram")
plt.xlabel("grayscale value")
plt.ylabel("pixels")

plt.plot(histogram, label='Original Image')  # <- or here
plt.plot(histogram1, label='Equalised Image')  # <- or here

conda create -n test python=3.8.5 pandas scipy numpy matplotlib seaborn jupyterlab

import matplotlib.pyplot as plt

plt.subplot()

import numpy as np
import matplotlib.pyplot as plt 

a= np.zeros([256*6,256*6, 3], dtype=np.uint8) # init the array
# fill the array with rgb values to create the spectrum without the use of loops

#red
a[:,:,0] = np.concatenate(([255]*256, np.linspace(255,0,256), [0]*256, [0]*256, np.linspace(0,255,256), [255]*256))

#green
a[:,:,1] = np.concatenate((np.linspace(0,255,256), [255]*256, [255]*256, np.linspace(255,0,256), [0]*256,[0]*256))

#blue
a[:,:,2] = np.concatenate(([0]*256, [0]*256,np.linspace(0,255,256),[255]*256, [255]*256, np.linspace(255,0,256)))


plt.imshow(a)  # this is different than what I am looking for

#points.geojson
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": {"confidence": 0.67}, "geometry": { "type": "Point", "coordinates": [ 37.703471404215918, 26.541625492300192 ] } },
{ "type": "Feature", "properties": {"confidence": 0.76}, "geometry": { "type": "Point", "coordinates": [ 37.009744331225093, 26.710090585532761 ] } },
{ "type": "Feature", "properties": {"confidence": 0.94}, "geometry": { "type": "Point", "coordinates": [ 37.541708538306224, 26.160111944646022 ] } },
{ "type": "Feature", "properties": {"confidence": 0.52}, "geometry": { "type": "Point", "coordinates": [ 37.628566642215354, 25.917300595223857 ] } },
{ "type": "Feature", "properties": {"confidence": 0.46}, "geometry": { "type": "Point", "coordinates": [ 37.676499267124271, 26.653959791866598 ] } },
{ "type": "Feature", "properties": {"confidence": 0.55}, "geometry": { "type": "Point", "coordinates": [ 37.677033863264533, 26.654033815175087 ] } },
{ "type": "Feature", "properties": {"confidence": 0.12}, "geometry": { "type": "Point", "coordinates": [ 37.37522057234797, 26.353271000367258 ] } },
{ "type": "Feature", "properties": {"confidence": 0.62}, "geometry": { "type": "Point", "coordinates": [ 37.396556958266373, 26.459196264023291 ] } },
{ "type": "Feature", "properties": {"confidence": 0.21}, "geometry": { "type": "Point", "coordinates": [ 36.879775221618168, 26.901743663072878 ] } }
]
}

import geoplot as gplt
import geopandas as gpd
import geoplot.crs as gcrs
import matplotlib.pyplot as plt

points = gpd.read_file('points.geojson')
polygons = gpd.read_file('polygons.geojson')

ax = gplt.polyplot(polygons, projection=gcrs.AlbersEqualArea(), zorder=1)
gplt.kdeplot(points, cmap='Reds', shade=True, clip=polygons, ax=ax) 
#weight=points.confidence don’t work inside kdeplot()

plt.show()