def print_hist():
global file_path
hist_box = Tk()
hist_box.title("Histogram")
if file_path == "":
Label(hist_box, text="Please select file first!").pack()
exit = Button(hist_box,
text="Exit",
command=lambda: hist_box.destroy()).pack()
else:
ans = []
hist(ans, file_path)
canvas = Canvas(hist_box, width=1000, height=600)
canvas.pack()
img = ImageTk.PhotoImage(Image.open("./histogram.png"), master=canvas)
canvas.create_image(0, 0, anchor=NW, image=img)
text = "Most frequent used word is : " + str(
ans[0][0]) + "\n" + "Least frequent used word is : " + str(
ans[0][1]) + "\n" + "Number of lines used in file: " + str(
ans[0][2])
label = Label(hist_box, text=text).pack()
exit = Button(hist_box,
text="Exit",
command=lambda: hist_box.destroy()).pack()
mainloop()
def reporter(image_file=IMAGE_FILE,
neighborhood=NEIGHBORHOOD,
scale=SCALE,
dpi=DPI,
channel=CHANNEL,
plane=PLANE,
scope=SCOPE):
"""
Test usage:
reporter('image.tiff')
reporter('image.tiff', 8, 5, 1000)
reporter('image.tiff', 8, 5, 500, 'R', 0, 5)
"""
bp(image_file, dpi, channel, plane)
hist(image_file, scale, dpi)
atc(image_file, dpi)
ngbd(image_file, neighborhood, dpi)
entropy(image_file)
def generate_sentence():
my_file = get_all_words("book.txt")
my_histogram = hist(my_file)
sentence = ""
num_words = 10
for _ in range(num_words):
word = sample(my_histogram)
sentence += " " + word
return sentence
async def turbid(turbidModel: TurbidModel):
img_s = stringToRGB(turbidModel.skyImage)
hist.hist(img_s, "sky.png")
img_s = crop_img(img_s)
b_s, g_s, r_s = cv2.split(img_s)
img_w = stringToRGB(turbidModel.waterImage)
hist.hist(img_w, "water.png")
img_w = crop_img(img_w)
b_w, g_w, r_w = cv2.split(img_w)
img_c = stringToRGB(turbidModel.greyImage)
hist.hist(img_c, "grey.png")
img_c = crop_img(img_c)
b_c, g_c, r_c = cv2.split(img_c)
if turbidModel.DN_s is None:
Rs = mean(r_s)
else:
Rs = turbidModel.DN_s
if turbidModel.DN_w is None:
Rw = mean(r_w)
else:
Rw = turbidModel.DN_w
if turbidModel.DN_c is None:
Rc = mean(r_c)
else:
Rc = turbidModel.DN_c
if (turbidModel.alpha, turbidModel.S) is (None, None):
Ls = radiance(Rs)
Lw = radiance(Rw)
Lc = radiance(Rc)
else:
Ls = radiance(Rs, turbidModel.alpha, turbidModel.S)
Lw = radiance(Rw, turbidModel.alpha, turbidModel.S)
Lc = radiance(Rc, turbidModel.alpha, turbidModel.S)
Rrs = reflectance(Ls, Lw, Lc)
turbid = turbidity(Rrs)
return {"turbidity": turbid, "waterHist": RGBTostring("water.png"), "skyHist": RGBTostring("sky.png"), "greyHist": RGBTostring("grey.png")}
alto, ancho = double_e.shape[:2]
mid = round(ancho / 2)
sec_R = [0, 0, mid, alto]
sec_L = [mid + 1, 0, ancho, alto]
# separamos el ojo izquierdo y derecho
Reye = double_e[sec_R[1]:sec_R[1] + sec_R[3],
sec_R[0]:sec_R[0] + sec_R[2]]
Leye = double_e[sec_L[1]:sec_L[1] + sec_L[3],
sec_L[0]:sec_L[0] + sec_L[2]]
######################## EXTRACÍÓN DE PROPIEDADES PARA DETECCIÓN ##################
# HISTOGRAMA
hR = h.hist(Reye)
hL = h.hist(Leye)
hR_blanco = hR[0]
hR_negro = hR[-1]
hL_blanco = hL[0]
hL_negro = hL[-1]
# PROMEDIO
mean_R = Reye.mean() * (Reye.shape[0] * Reye.shape[1])
mean_L = Leye.mean() * (Leye.shape[0] * Leye.shape[1])
# DESVIACIÓN ESTANDAR
std_R = Reye.std() * (Reye.shape[0] * Reye.shape[1])
std_L = Leye.std() * (Leye.shape[0] * Leye.shape[1])
#!/usr/bin/env python import numpy as np import histogram import integrator as intr import matplotlib as plt N =100000 A = np.zeros(N) b = 5 n = 1000 for i in range(N): A[i] = intr.exponential(b) histogram.hist(A,n) #problem 2 [w,Sw] = intr.monte_carlo_3d(intr.density,[-.5,-.5,-1],[1,1,1],intr.sphere,100000) [x,Sw] = intr.monte_carlo_3d(intr.xmoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000) [y,Sw] = intr.monte_carlo_3d(intr.ymoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000) [z,Sw] = intr.monte_carlo_3d(intr.zmoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000) print(x/w,y/w,z/w)
def build_dictogram(self, word_list):
'''Creates a histogram dictionary using the word_list property and returns it'''
return hist(word_list)