如何在 Python 中使用 OpenCV 提高 Caffe 的性能？

Question

我正在按照本教程Face detection with OpenCV and deep learning 使用 OpenCV3、Caffe 和 Python3 创建和人脸检测软件。 这是使用的代码：

    # USAGE
    # python detect_faces.py --image rooster.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel

# import the necessary packages
import numpy as np
import argparse
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
                help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
                help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.40,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
print(h)
dimension_x =h
dimension_y=h
print(image.shape[:2])
blob = cv2.dnn.blobFromImage(cv2.resize(image, (dimension_x, dimension_y)), 1.0, (dimension_x, dimension_y), (104.0, 177.0, 123.0))
# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()

print(detections)

# loop over the detections
for i in range(0, detections.shape[2]):

    # extract the confidence (i.e., probability) associated with the
    # prediction
    confidence = detections[0, 0, i, 2]

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # compute the (x, y)-coordinates of the bounding box for the
        # object
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        print(confidence, startX, startY, endX, endY )
        print(box)
        # draw the bounding box of the face along with the associated
        # probability
        text = "{:.2f}%".format(confidence * 100)
        y = startY - 10 if startY - 10 > 10 else startY + 10
        cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
        cv2.putText(image, text, (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

print(type(image))
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)

当我使用此命令从命令行运行代码时：

python detect_faces.py  --prototxt  deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel --confidence=0.45 --image 14.jpg

我得到这个结果2: * Source*

结果很好，但是我们注意到在图片最左下角的位置，我画了一个蓝色圆圈，程序已经两次检测到女孩的脸。第一个检测是好的，但第二个不是！ 我在网上搜索了一种给模型/程序反馈的方法，这样它就会知道检测到的物体（被蓝色圆圈包围，准确率51.11%）不是人脸。所以它可以避免把它作为人脸返回！

所以我的问题是，如何微调使用的 Caffe 模型以排除被检测为人脸的非人脸对象，以用于未来的人脸检测任务？

我的问题不仅与这种特定情况有关，而且通常针对所有被检测为人脸但不是人脸的对象。使用的图像只是一个示例。

Answer 1

@Peshmerge，对于特定图像，您可以改变输入 blob 尺寸以获得最佳结果。 例如，900x900：

python object_detection.py --model opencv_face_detector.caffemodel --config opencv_face_detector.prototxt --mean 104 177 123  --thr 0.4 --input P1280471.JPG --width 900 --height 900

一个脚本：https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.py