def show_detected_face(result, detected, title = "Detected Faces"):
plt.imshow(result)
img_desc=plt.gca()
plt.set_cmap('gray')
plt.title(title)
plt.axis('off')
for patch in detected:
img_desc.add_patch(
Rectangle(
(patch['c'], patch['r'])
patch['width'], patch['height'], fill = False, color = 'g', line_width = 3
)
)
plt.show()
def fd():
from skimage.feature import Cascade
from skimage import data, color
import matplotib.pyplot as plt
from matplotlib.patches import Rectangle
path = ''
img = plt.imread(path)
plt.axis('off')
plt.imshow(img)
train_set = data.lbp_frontal_face_cascade_filename()
detector = Cascade(train_set)
detected = detector.detect_multi_scale(img=img,
scale_factor=1.2,
step_ratio=1,
min_size=(10, 10),
max_size=(200, 200))
print('Detected')
def show_detected_face(result, detected, title="Detected Faces"):
plt.imshow(result)
img_desc = plt.gca()
plt.set_cmap('gray')
plt.title(title)
plt.axis('off')
for rec in detected:
img_desc.add_patch(
Rectangle((rec['c'], rec['r']),
rec['width'],
rec['height'],
fill=False,
color='g',
line_width=3))
plt.show()
show_detected_face(img, detected)
image = cv2.imread(os.path.join(folder, filename))
if (image is not None):
images.append(image)
return images
def to_gray(image):
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
originPath = ""
targetFolder = ""
targetImages = load_images_from_folder(targetFolder)
originImage = cv2.imread(originPath)
MAX = 2**8 - 1
for i in range(0, len(targetImages)):
plt.imshow(to_gray(targetImages[i]))
plt.show()
mse = compare_mse(originImage, targetImages[i])
print("mse:{}", format(mse))
psnr = compare_psnr(originImage, targetImages[i])
print("msnr:{}", format(psnr))
ssim = compare_ssim(originImage, targetImages[i])
print("psnr:{}", format(ssim))
plt.hist(df['deep learning'], bins=15)
plt.show() #siempre se pone al final
plt.clf() #y eso para limpiar la grafica. Poner antes de hacer otra
column_list2 = ['Temperature (deg F)','Dew Point (deg F)']
df[column_list2].plot() #plotear solo algunas columnas, guardadas como una lista en column_list2
plt.xscale('log') #eje x logaritmico
plt.xlabel('xlab')
plt.ylabel('ylab')
plt.title('title')
tick_val = [1000, 10000, 100000] #ticks donde se pone el tick lab
tick_lab = ['1k', '10k', '100k']
plt.xticks(tick_val, tick_lab)
plt.xlim(20, 55)
plt.ylim(20, 55)
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')
plt.grid(True)
plt.hist(life_exp, bins = 5) #histogramas
plt.imshow(im_sq, cmap='Greys', interpolation='nearest') #una imagen de 28*28 pixels
plt.show()
df['Existing Zoning Sqft'].plot(kind='scatter', x='Year', y='Total Urban Population') #kind="hist", "box", logx=True, logy=True
df.boxplot(column="initial_cost", by="Borough", rot=90)
from skimage.feature import Cascade
from skimage import data, color
import matplotib.pyplot as plt
from matplotlib.patches import Rectangle
path=''
img = plt.imread(path)
plt.axis('off')
plt.imshow(img)
train_set = data.lbp_frontal_face_cascade_filename()
detector = Cascade(train_set)
detected = detector.detect_multi_scale(img=img, scale_factor = 1.2, step_ratio = 1, min_size = (10,10), max_size = (200,200))
print('Detected')
def show_detected_face(result, detected, title = "Detected Faces"):
plt.imshow(result)
img_desc=plt.gca()
plt.set_cmap('gray')
plt.title(title)
plt.axis('off')
for patch in detected:
img_desc.add_patch(
Rectangle(
(patch['c'], patch['r'])
patch['width'], patch['height'], fill = False, color = 'g', line_width = 3
)
)
plt.show()