From 2753ec991be9ec864dfd176bc5df8887c2bdcb2a Mon Sep 17 00:00:00 2001 From: Yuukiy <76897913+Yuukiy@users.noreply.github.com> Date: Sat, 25 Dec 2021 19:32:45 +0800 Subject: [PATCH] =?UTF-8?q?=E6=97=8B=E8=BD=AC=E5=9B=BE=E7=89=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=88=B0=E4=BA=BA=E8=84=B8=E5=90=8E=E8=AE=A1=E7=AE=97?= =?UTF-8?q?=E6=97=8B=E8=BD=AC=E5=89=8D=E7=9A=84=E5=9B=BE=E7=89=87=E7=9A=84?= =?UTF-8?q?=E4=BA=BA=E8=84=B8=E4=B8=AD=E5=BF=83=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- core/opencv.py | 73 +++++++++++++++++++++++++++++++++++--------------- 1 file changed, 51 insertions(+), 22 deletions(-) diff --git a/core/opencv.py b/core/opencv.py index c354e70..b750dd2 100644 --- a/core/opencv.py +++ b/core/opencv.py @@ -1,12 +1,13 @@ """使用Open CV识别图像""" import os -import cv2 +import cv2 as cv import sys +import math sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from core.datatype import mei_path -face_cascade = cv2.CascadeClassifier(mei_path('data/haarcascade_frontalface_default.xml')) +face_cascade = cv.CascadeClassifier(mei_path('data/haarcascade_frontalface_default.xml')) # https://github.com/PyImageSearch/imutils/blob/master/imutils/convenience.py#L41 @@ -17,7 +18,7 @@ def rotate_bound(image, angle): # grab the rotation matrix (applying the negative of the angle to rotate clockwise), # then grab the sine and cosine (i.e., the rotation components of the matrix) - M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) + M = cv.getRotationMatrix2D((cX, cY), -angle, 1.0) cosine = abs(M[0, 0]) sine = abs(M[0, 1]) @@ -30,7 +31,7 @@ def rotate_bound(image, angle): M[1, 2] += (nH / 2) - cY # perform the actual rotation and return the image - return cv2.warpAffine(image, M, (nW, nH)) + return cv.warpAffine(image, M, (nW, nH)) def isValidArea(area): @@ -43,40 +44,68 @@ def isValidArea(area): return False +def calc_origin_area(img, area, rotate, ori_size): + """计算图片中指定区域的中心点在旋转前的位置""" + # 计算area的中心点以图片中心点为坐标原点的新坐标 + (h, w) = img.shape[:2] + Ox, Oy = (h//2, w//2) + new_x = area[0] + area[2]//2 - Ox + new_y = area[1] + area[3]//2 - Oy + # 计算area的中心点做反向旋转后的坐标 + anti_rotate = math.radians(-rotate) + anti_x = new_x * math.cos(anti_rotate) - new_y * math.sin(anti_rotate) + anti_y = new_x * math.sin(anti_rotate) + new_y * math.cos(anti_rotate) + # 计算area的中心点在原图坐标系中的坐标 + (ori_h, ori_w) = ori_size + origin_x = int(ori_w // 2 + anti_x) + origin_y = int(ori_h // 2 + anti_y) + return (origin_x, origin_y) + + def detect_faces(img_path, debug=False): - img = cv2.imread(img_path) - faces = [] + """检测图片中的人脸并返回第一个有效的人脸区域""" + img = cv.imread(img_path) # OpenCV的检测效果似乎受图像角度影响很大，因此要尝试旋转图片 rotate_angles = (0, 10, -10, 20, -20, 30, -30, 40, -40, 50, -50, 60, -60) for angle in rotate_angles: rotated = rotate_bound(img, angle) - grey = cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY) + grey = cv.cvtColor(rotated, cv.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(grey, 1.1, 4) for area in faces: if isValidArea(area) and (not debug): - return area + ori_pos = calc_origin_area(rotated, area, angle, img.shape[:2]) + return ori_pos # 有效区域用绿框标记，无效区域用红框标记 color = (0, 255, 0) if isValidArea(area) else (0, 0, 255) (x, y, w, h) = area - cv2.rectangle(rotated, (x, y), (x+w, y+h), color, 2) + cv.rectangle(rotated, (x, y), (x+w, y+h), color, 2) recog_info = f'({x},{y}), {w}x{h}' (h2, w2) = rotated.shape[:2] pos = (min(w2-170, x), max(y-10, 12)) # 避免信息显示到绘图区外 - cv2.putText(rotated, recog_info, pos, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) + cv.putText(rotated, recog_info, pos, cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) if debug: win_name = "Actress detection" - cv2.imshow(win_name, rotated) - cv2.setWindowTitle(win_name, win_name + f" with ratotion {angle} degrees") - cv2.waitKey(0) - return faces + cv.imshow(win_name, rotated) + cv.setWindowTitle(win_name, win_name + f" with ratotion {angle} degrees") + cv.waitKey(0) + + +def crop_by_face(img_path, debug=False): + """根据指定的人脸区域裁剪图片""" + face_center = detect_faces(img_path) + if debug: + if not os.path.exists('debug'): + os.mkdir('debug') + img = cv.imread(img_path) + cv.circle(img, face_center, 100, (0,255,0), 1) + filepath, ext = os.path.splitext(img_path) + output = 'debug/' + filepath + '_opencv' + ext + cv.imwrite(output, img) + return face_center if __name__ == "__main__": - import argparse - # construct the argument parse and parse the arguments - ap = argparse.ArgumentParser() - ap.add_argument("-i", "--image", required=True, - help="path to the image file") - args = vars(ap.parse_args(['-i', 'YPPx.jpg'])) - - detect_faces(args['image'], True) + if len(sys.argv) < 2: + sys.argv.append('siro-4701.jpg') + for img_path in sys.argv[1:]: + crop_by_face(img_path, True)