Question

我正在按照本教程（https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/#post_downloads）使用“ res10_300x300_ssd_iter_140000.caffemodel”创建人脸检测程序。另外，我发现了另一篇文章（How to improve the performance of Caffe with OpenCV in Python?）。从这篇文章中，我发现我可以用不同的大小调整输入图像的大小，以在人脸检测上获得更好的准确性。例如，将大小调整为900 x 900而不是300 x 300，以便在检测单个图像中的多个面部时获得更好的结果。

但是有一个问题，模型的输入大小是300 x 300，为什么我们可以只用不同的分辨率调整输入图像的大小？在人类视觉的概念中，将尺寸调整为正方形图像会水平或垂直地挤压我们的脸，就像某些概念“调整脸部特征的大小不会改变很多”吗？

这是下面的代码：

# 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=0.50, 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")
        # 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)

如何在Python中基于Caffe改善面部检测模型的性能？

0 个答案: