def labelview(labels, filename=None):
"""
Visualización de labels asignados con colores.
Args:
labels (Numpy nd Array): arreglo en formato de imagen con labels indicados
filename (string): string con el nombre del archivo a guardar
Returns:
Visualización (plot.imgview): imagen con los colores asignados en lugar de las etiquetas
"""
nueva = np.zeros((len(labels), len(labels[0])), dtype=int).astype(np.uint8)
if len(nueva.shape) == 2:
nueva = cv.cvtColor(nueva, cv.COLOR_GRAY2RGB)
colores = [[], []]
for i in range(len(labels)):
for j in range(len(labels[i])):
valor = labels[i, j]
if valor != 0:
if valor in colores[0]:
indice = colores[0].index(valor)
color = colores[1][indice]
else:
color = [
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255)
]
colores[0].append(valor)
colores[1].append(color)
nueva[i, j] = color
plot.imgview(nueva, None, None, filename)
def labelview(labels):
if len(labels.shape) == 2:
labels = cv.cvtColor(labels, cv.COLOR_GRAY2RGB)
colores = [[], []]
for i in range(len(labels)):
for j in range(len(labels[i])):
valor = labels[i, j][0]
if valor != 0:
if valor in colores[0]:
indice = colores[0].index(valor)
color = colores[1][indice]
else:
color = [
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255)
]
colores[0].append(valor)
colores[1].append(color)
labels[i, j] = color
plot.imgview(labels)
import math
get_ipython().run_line_magic('matplotlib', 'inline')
# In[15]:
img = cv.imread("./wikipedia.png", cv.IMREAD_GRAYSCALE)
# In[16]:
print(img.shape)
plot.imgview(img)
normalizada = plot.imgnorm(img)
# In[17]:
hello = plot.hist(img)
print(normalizada.shape)
# In[18]:
plot.hist(normalizada)
plot.imgview(normalizada)
else:
color = [
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255)
]
colores[0].append(valor)
colores[1].append(color)
labels[i, j] = color
plot.imgview(labels)
# In[82]:
huella = cv.imread('./intentar_wiki.pgm', cv.IMREAD_GRAYSCALE)
plot.imgview(huella)
# In[83]:
import sys
np.set_printoptions(threshold=sys.maxsize)
# In[118]:
img = cv.imread('./fprint3.pgm', cv.IMREAD_GRAYSCALE)
img = img[300:500, 300:500]
plot.imgview(img)
print(len(img))
print(len(img[0]))
# In[119]:
def hit_miss(img, kernel_hit, kernel_miss):
img2 = cv.bitwise_not(img)
erode_1 = cv.erode(img, kernel_hit, iterations=1)
erode_2 = cv.erode(img2, kernel_miss, iterations=1)
return cv.bitwise_and(erode_1, erode_2)
# In[4]:
imagen = cv.imread('./intentar.pgm', cv.IMREAD_GRAYSCALE)
# In[5]:
plot.imgview(imagen, True)
# In[6]:
kernel_hit = np.array([[0, 255, 0], [0, 255, 0], [0, 255, 0]], np.uint8)
kernel_miss = np.array([[255, 0, 255], [255, 0, 255], [255, 0, 255]], np.uint8)
# In[7]:
plot.imgcmp(kernel_hit, kernel_miss, True)
# In[8]:
plot.imgview(hit_miss(imagen, kernel_hit, kernel_miss), True)
# In[9]:
# In[1]:
import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
import plot
get_ipython().run_line_magic('matplotlib', 'inline')
# In[43]:
img = cv.imread('./ufmlogo.png', 0)
#img = cv.blur(img, (8, 8))
#ret,img = cv.threshold(img,100,255,cv.THRESH_BINARY)
img = cv.Canny(img, 50, 200)
plot.imgview(img)
# In[4]:
img2 = cv.imread('./encontrar.png', cv.IMREAD_GRAYSCALE)
img3 = cv.imread('./encontrar.png')
img3 = cv.cvtColor(img3, cv.COLOR_BGR2RGB)
plot.imgview(img3)
# In[5]:
template = img2[250:450, 420:600]
plot.imgview(template)
# In[57]:
import plot
from mpl_toolkits.mplot3d import axes3d
plt.style.use('dark_background')
get_ipython().run_line_magic('matplotlib', 'inline')
# In[3]:
img = cv.imread("./wikipedia.png", cv.IMREAD_GRAYSCALE)
img = plot.equalizar(img)
plot.imgview(img)
# In[4]:
thresh_val = 80
ret, thresh = cv.threshold(img, thresh_val, 255, cv.THRESH_BINARY)
# In[5]:
print(ret)
b_y = convolve(b, gy) # In[169]: mag1 = float64_to_uint8(gradient2magnitude(r_x, r_y)) mag2 = float64_to_uint8(gradient2magnitude(g_x, g_y)) mag3 = float64_to_uint8(gradient2magnitude(b_x, b_y)) # In[170]: final = cv.merge((mag1, mag2, mag3)) plot.imgview(final, None, None, "rgb_magnitudes") # In[216]: ang1 = float64_to_uint8(gradient2angle(r_x, r_y)) ang2 = float64_to_uint8(gradient2angle(g_x, g_y)) ang3 = float64_to_uint8(gradient2angle(b_x, b_y)) # In[217]: final = cv.merge((ang1, ang2, ang3)) plot.imgview(final, None, None, "rgb_angles")