真实应用

Python 图像处理

Rebeca Gonzalez

Data engineer

应用

边缘/角点检测前转为灰度
去噪与图像修复
模糊已检测的人脸
物体尺寸近似估计

隐私保护

# Import Cascade of classifiers and gaussian filter
from skimage.feature import Cascade

from skimage.filters import gaussian

隐私保护

# Detect the faces
detected = detector.detect_multi_scale(img=image, 
                                       scale_factor=1.2, step_ratio=1, 
                                       min_size=(50, 50), max_size=(100, 100))

# For each detected face
for d in detected:  
    # Obtain the face cropped from detected coordinates
    face = getFace(d)

隐私保护

def getFace(d):
    ''' Extracts the face rectangle from the image using the
    coordinates of the detected.'''
    # X and Y starting points of the face rectangle
    x, y  = d['r'], d['c']


    # The width and height of the face rectangle
    width, height = d['r'] + d['width'],  d['c'] + d['height']


    # Extract the detected face
    face= image[x:width, y:height]
    return face

隐私保护

# Detect the faces
detected = detector.detect_multi_scale(img=image, 
                                       scale_factor=1.2, step_ratio=1, 
                                       min_size=(50, 50), max_size=(100, 100))

# For each detected face
for d in detected:  
    # Obtain the face cropped from detected coordinates
    face = getFace(d)


    # Apply gaussian filter to extracted face
    gaussian_face = gaussian(face, multichannel=True, sigma = 10)


    # Merge this blurry face to our final image and show it
    resulting_image = mergeBlurryFace(image, gaussian_face)

隐私保护

def mergeBlurryFace(original, gaussian_image):
    # X and Y starting points of the face rectangle
    x, y  = d['r'], d['c']
    # The width and height of the face rectangle
    width, height = d['r'] + d['width'],  d['c'] + d['height']

    original[ x:width, y:height] =  gaussian_image
    return original

隐私保护

让我们来练习！

Python 图像处理

真实应用

应用

隐私保护

隐私保护

隐私保护

隐私保护

隐私保护

隐私保护

隐私保护

更多案例

让我们来练习！