Python 调整图像大小时，模糊会导致人脸和眼睛检测

我正在使用Python在OpenCV中尝试人脸和眼睛检测代码。该代码适用于图像大小为2848 X 4272的情况，甚至当我将其大小调整为0.5倍时也能正常工作。但是，每当我用其他因素（如0.2、0.4等）调整它的大小时，它会给我的眼睛（如额头、鼻子的几个区域）带来模糊的结果。在这种情况下，我无法得到所有图像大小的通用代码。是否有任何代码，使我得到正确的检测与任何图像大小，因为它是非常困难的处理这样大的图像。守则是这样的

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

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例如，随着图像变得越来越小，区分眼睛和鼻子就变得越来越困难，这是合乎逻辑的。因此，除非您从根本上了解图像分析功能需要什么（我不知道），否则很难知道缩小图像尺寸的最佳方法，同时保留分析所需的信息类型

话虽如此，我相信它比cv2更常用于收缩图像。INTER_LINEAR等

请尝试以下操作，而不是调整现有的大小：

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

另外，通过改变图像的纵横比（fx！=fy），你不是更难识别眼睛了吗？如果没有特殊原因，可以使用第二个position参数size显式选择目标大小。例如：

effective_but_smaller_size = (640, 480)  # or whatever you find works
img = cv2.resize(img11, effective_but_smaller_size, interpolation=cv2.INTER_AREA)

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等等，我会让你知道的