目录

图像识别

人脸识别

鸟图识别

一、人脸识别：

即使是地铁挤压的人脸，也是有尊严的，值得被检测，经过 OpenCV 的努力，成功检测：

• 左图是正常被识别的人脸
• 中图由于挤地铁人脸已不可识别
• 右图OpenCV单应性变换后，拯救了被miss的人脸

实现代码如下：

import cv2
import numpy as np


def correct_rust_face():
    img_src = cv2.imread('rust_face_src.jpeg')
    pts_src = np.array([[0, 0], [0, 1078], [570, 0], [570, 1078]])

    img_dst = cv2.imread('rust_face_dest.jpeg')
    pts_dst = np.array([[63, 285], [84, 820], [378, 224], [427, 689]])

    dw, dh = img_src.shape[1], img_src.shape[0]

    h, status = cv2.findHomography(pts_dst, pts_src)
    img_out = cv2.warpPerspective(img_dst, h, (dw, dh))
    return img_out


def face_detect(input_image):
    [x, y, w, h] = [0, 0, 0, 0]

    # TODO(You): 实现人脸识别
    h, w = input_image.shape[:2]
    gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
    gray = cv2.equalizeHist(gray)
    min_size = (w // 10, h // 10)

    face_cascade = cv2.CascadeClassifier(
    "data_haarcascades_haarcascade_frontalface_alt.xml")

    face_rects = face_cascade.detectMultiScale(gray, 1.05, 2, cv2.CASCADE_SCALE_IMAGE, min_size)

    if len(face_rects) > 0:
        for face_rect in face_rects:
            x, y, w, h = face_rect
            cv2.rectangle(input_image, (x, y),
                          (x + w, y + h), [255, 255, 0], 2)


if __name__ == '__main__':
    [x, y, w, h] = [0, 0, 0, 0]

    rust_face_src = cv2.imread('rust_face_src.jpeg')
    face_detect(rust_face_src)

    rust_face_dest = cv2.imread('rust_face_dest.jpeg')
    face_detect(rust_face_dest)

    rust_face_dest_correct = correct_rust_face()
    face_detect(rust_face_dest_correct)

    images = np.concatenate(
        (rust_face_src[0: 1070, 0:542, 0:3], rust_face_dest, rust_face_dest_correct[0: 1070, 0:542, 0:3]), axis=1)
    cv2.imshow('rust_face_detect', images)

    cv2.waitKey(0)
    cv2.destroyAllWindows()

 说明：""data_haarcascades_haarcascade_frontalface_alt.xml""文件可以从OpenCV 官方仓库里人脸分类器模型去下载。

二、鸟图识别

使用基本的OpenCV轮廓检测识别出野外拍摄照片里的鸟：

基本架构代码如下：

mport cv2

if __name__ == '__main__':
    img = cv2.imread('bird.jpeg', -1)
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    ret, img_binary = cv2.threshold(
        img_gray, 127, 255, cv2.THRESH_BINARY)
    ret, bin_img = cv2.threshold(
        img_binary, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

    # findCountours 函数在版本4之后返回参数只有2个
    if (int(cv2.__version__[0]) > 3):
        contours, hierarchy = cv2.findContours(
            bin_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    else:
        img2, contours, hierarchy = cv2.findContours(
            bin_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # TODO(You): 请在此实现代码
    sorted_contours = sorted(contours, key=cv2.contourArea, reverse=False)
    max_contour = sorted_contours[-2]
    max_contour_area = cv2.contourArea(max_contour)

                                                                      
                                                                      
    bird_contours = filter(lambda c: abs(cv2.contourArea(c)-max_contour_area) < 1000, sorted_contours)

    for c in bird_contours:
        x, y, w, h = cv2.boundingRect(c)
        cv2.rectangle(img, (x-10, y-100),
                    (x+w+10, y+h+10), (0, 255, 0), 2)
        cv2.drawContours(img, c, -1, (0, 0, 255), 3)


    cv2.imwrite('birds_detect.jpeg', img)
    cv2.imshow("birds", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()