test/howcuteami.py

import cv2
import math
import argparse
import numpy as np
import os


# detect face
def highlightFace(net, frame, conf_threshold=0.95):
    frameOpencvDnn = frame.copy()
    frameHeight = frameOpencvDnn.shape[0]
    frameWidth = frameOpencvDnn.shape[1]
    blob = cv2.dnn.blobFromImage(
        frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False
    )

    net.setInput(blob)
    detections = net.forward()
    faceBoxes = []

    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > conf_threshold:
            x1 = int(detections[0, 0, i, 3] * frameWidth)
            y1 = int(detections[0, 0, i, 4] * frameHeight)
            x2 = int(detections[0, 0, i, 5] * frameWidth)
            y2 = int(detections[0, 0, i, 6] * frameHeight)
            faceBoxes.append(scale([x1, y1, x2, y2]))

    return faceBoxes


# scale current rectangle to box
def scale(box):
    width = box[2] - box[0]
    height = box[3] - box[1]
    maximum = max(width, height)
    dx = int((maximum - width) / 2)
    dy = int((maximum - height) / 2)

    bboxes = [box[0] - dx, box[1] - dy, box[2] + dx, box[3] + dy]
    return bboxes


# crop image
def cropImage(image, box):
    num = image[box[1] : box[3], box[0] : box[2]]
    return num


# main
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--image", type=str, required=False, help="input image")
args = parser.parse_args()

# 创建输出目录
output_dir = "../output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

faceProto = "models/opencv_face_detector.pbtxt"
faceModel = "models/opencv_face_detector_uint8.pb"
ageProto = "models/age_googlenet.prototxt"
ageModel = "models/age_googlenet.caffemodel"
genderProto = "models/gender_googlenet.prototxt"
genderModel = "models/gender_googlenet.caffemodel"
beautyProto = "models/beauty_resnet.prototxt"
beautyModel = "models/beauty_resnet.caffemodel"

MODEL_MEAN_VALUES = (104, 117, 123)
ageList = [
    "(0-2)",
    "(4-6)",
    "(8-12)",
    "(15-20)",
    "(25-32)",
    "(38-43)",
    "(48-53)",
    "(60-100)",
]
genderList = ["Male", "Female"]

# 定义性别对应的颜色 (BGR格式)
gender_colors = {
    "Male": (255, 165, 0),  # 橙色 Orange
    "Female": (255, 0, 255),  # 洋红 Magenta / Fuchsia
}

faceNet = cv2.dnn.readNet(faceModel, faceProto)
ageNet = cv2.dnn.readNet(ageModel, ageProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)
beautyNet = cv2.dnn.readNet(beautyModel, beautyProto)

# 读取图片
image_path = args.image if args.image else "images/charlize.jpg"
frame = cv2.imread(image_path)

if frame is None:
    print(f"无法读取图片: {image_path}")
    exit()

faceBoxes = highlightFace(faceNet, frame)
if not faceBoxes:
    print("No face detected")
    exit()

print(f"检测到 {len(faceBoxes)} 张人脸")

for i, faceBox in enumerate(faceBoxes):
    # 提取人脸区域
    face = cropImage(frame, faceBox)
    face_resized = cv2.resize(face, (224, 224))

    # gender net
    blob = cv2.dnn.blobFromImage(
        face_resized, 1.0, (224, 224), MODEL_MEAN_VALUES, swapRB=False
    )
    genderNet.setInput(blob)
    genderPreds = genderNet.forward()
    gender = genderList[genderPreds[0].argmax()]
    print(f"Gender: {gender}")

    # age net
    ageNet.setInput(blob)
    agePreds = ageNet.forward()
    age = ageList[agePreds[0].argmax()]
    print(f"Age: {age[1:-1]} years")

    # beauty net
    blob = cv2.dnn.blobFromImage(
        face_resized, 1.0 / 255, (224, 224), MODEL_MEAN_VALUES, swapRB=False
    )
    beautyNet.setInput(blob)
    beautyPreds = beautyNet.forward()
    beauty = round(2.0 * sum(beautyPreds[0]), 1)
    print(f"Beauty: {beauty}/10.0")

    # 根据性别选择颜色
    color = gender_colors[gender]

    # 保存人脸图片 - 使用cv2.imwrite
    face_filename = f"{output_dir}/face_{i+1}.webp"
    cv2.imwrite(face_filename, face, [cv2.IMWRITE_WEBP_QUALITY, 95])
    print(f"人脸图片已保存: {face_filename}")

    # 保存评分到图片上（可选）
    face_with_text = face.copy()
    cv2.putText(
        face_with_text, f"{gender}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2
    )
    cv2.putText(
        face_with_text,
        f"{age[1:-1]} years",
        (10, 60),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.7,
        color,
        2,
    )
    cv2.putText(
        face_with_text,
        f"{beauty}/10.0",
        (10, 90),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.7,
        color,
        2,
    )

    annotated_filename = f"{output_dir}/face_{i+1}_annotated.webp"
    cv2.imwrite(annotated_filename, face_with_text, [cv2.IMWRITE_WEBP_QUALITY, 95])
    print(f"标注人脸已保存: {annotated_filename}")

    # 在原图上绘制人脸框和信息
    cv2.rectangle(
        frame,
        (faceBox[0], faceBox[1]),
        (faceBox[2], faceBox[3]),
        color,
        int(round(frame.shape[0] / 400)),
        8,
    )
    cv2.putText(
        frame,
        f"{gender}, {age}, {beauty}",
        (faceBox[0], faceBox[1] - 10),
        cv2.FONT_HERSHEY_SIMPLEX,
        1.25,
        color,
        2,
        cv2.LINE_AA,
    )

# 保存完整的标注图片
result_filename = f"{output_dir}/result_full.webp"
cv2.imwrite(result_filename, frame, [cv2.IMWRITE_WEBP_QUALITY, 95])
print(f"完整结果图片已保存: {result_filename}")

# 显示图片
cv2.imshow("howbeautifulami", frame)
cv2.waitKey(0)
cv2.destroyAllWindows()