调整图像大小时人脸和眼睛检测中的模糊结果

Question

我正在使用Python在opencv中尝试一个人脸和眼睛检测代码。这段代码适用于2848x4272的图像，甚至当我将其调整为0.5倍时也是如此。但是每当我用其他因素如0.2，0.4等来调整它的大小时，它会给我一个模糊的眼睛结果（比如额头、鼻子的几个区域），在这种情况下，我无法得到一个所有图像大小的通用代码。有没有什么代码可以让我在任何图像大小下都能得到正确的检测，因为处理这么大的图像非常困难。守则就是这样

import numpy as np
import cv2
import cv2.cv as cv

#attaching the haar cascade files
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

# reading the image
img11 = cv2.imread('IMG_0347.JPG')

if img11 !=None:

# resizing the image
    w,h,c= img11.shape
    print "dimension"
    print w,h
    img = cv2.resize(img11,None,fx=0.4, fy=0.3, interpolation =   cv2.INTER_LINEAR)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # converting into grayscale
    gray = cv2.equalizeHist(gray)
    #cv2.imshow('histo',gray)
    w,h,c= img.shape # finding out the dimensions of the image i.e width, height and number of channels

# creating a white background of same dimensions as input image for pasting the eyes detected by 'haarcascade_eye.xml'
    im = np.zeros((w,h,c),np.uint8)
    im[:]=[255,255,255]

# creating a white background of same dimensions as input image for pasting the masked eyes
    im_mask = np.zeros((w,h,c),np.uint8)
    im_mask[:]=[255,255,255]

# faces gives the top left coordinates of the detected face and width and height of the rectangle
    faces = face_cascade.detectMultiScale(gray, 1.5, 5)

# taking face as the ROI
    for (x,y,w,h) in faces:
                cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),1) # Draws the rectangle around the detected face
                roi_gray = gray[y:y+h, x:x+w]
                roi_color = img[y:y+h, x:x+w]
                #cv2.imshow('image1',img)  # shows the original image with face detected
                #cv2.imshow('image1',roi_color)  # shows only the face detected (colored)

# searching for eyes in the detected face i.e in the roi gray
                eyes = eye_cascade.detectMultiScale(roi_gray)
#print eyes # prints the top left coordinates of the detected eyes and width and height of the rectangle
                if eyes.any():
                    for (ex,ey,ew,eh)in eyes:
                        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),1) # draws rectangle around the masked eyes
                        eye_mask= roi_color[ey+1:u, ex+1:ex+ew]                             # eye_mask is the masked portion of the detected eye extracted from roi_color
                        im_mask[ey+1+y:y+u, ex+x+1:ex+ew+x]=eye_mask         #pasting the eye_mask on the white background called im_mask
                else:
                        print ("eyes could not be detected")

            cv2.imshow('image',im_mask)   #shows the im-mask white background with masked eyes pasted on it

Answer 1

例如，随着图像越来越小，很难区分眼睛和鼻子，这是合乎逻辑的。因此，除非你从根本上了解你的图像分析功能在寻找什么（我没有），否则很难知道在保留分析所需的信息类型的同时缩小图像尺寸的最佳方法。在

话虽如此，我相信^{}比cv2.INTER_LINEAR等更常用于压缩图像

您可以尝试以下方法来调整大小：

img = cv2.resize(img11, None, fx=0.4, fy=0.3, interpolation=cv2.INTER_AREA)

另外，你是不是通过改变图像的纵横比（fx）来增加识别眼睛的难度！=fy）？如果您没有特殊的原因，您可以使用第二个position参数size显式地选择目标大小。例如：

^{pr2}$