很久没有写CV方面的文章了，最近笔者接触了一段时间的目标检测的工具：darknet，因此希望能写篇文章记录下，同时希望自己能在目标检测有更深一步的研究。
目标检测是计算机视觉和数字图像处理的一个热门方向，广泛应用于机器人导航、智能视频监控、工业检测、航空航天等诸多领域，通过计算机视觉减少对人力资本的消耗，具有重要的现实意义。目标检测即找出图像中所有感兴趣的物体，包含物体定位和物体分类两个子任务，同时确定物体的类别和位置。图像分类的任务我们已经熟悉了，而目标检测与图片分类的不同之处在于，目标检测需要检测出图片中我们感兴趣的物体，同时检测出其边界并给出类别。
目前主流的目标检测算法主要是基于深度学习模型，大概可以分成两大类别：
- One-Stage目标检测算法，这类检测算法不需要产生候选区域(Region Proposal)阶段，可以通过一个Stage直接产生物体的类别概率和位置坐标值，比较典型的算法有YOLO、SSD和CornerNet；
- Two-Stage目标检测算法，这类检测算法将检测问题划分为两个阶段，第一个阶段首先产生候选区域，包含目标大概的位置信息，然后第二个阶段对候选区域进行分类和位置精修，这类算法的典型代表有R-CNN，Fast R-CNN，Faster R-CNN等。
目标检测模型的主要性能指标是检测准确度和速度，其中准确度主要考虑物体的定位以及分类准确度。一般情况下，Two-Stage算法在准确度上有优势，而One-Stage算法在速度上有优势。
darknet是一个开源的深度学习框架，它实现了YOLO算法，官方网站地址为：https://pjreddie.com/darknet/yolo/。基于它，我们能够很多有意思的事情。本文的后续部分将会给出darknet在图片和视频中进行目标检测的例子。
图像中的目标检测
这部分将会给出darknet在图片中进行目标检测的例子。
首先我们先把darknet的源码下载到本地：
git clone https://github.com/pjreddie/darknet.git 然后我们下载已经预先训练好的YOLO3算法的模型yolov3，下载网址为：https://pjreddie.com/media/files/yolov3.weights，下载文件放在backup文件夹下。注意，该模型是在COCO数据集上训练的结果，COCO数据集包含80个类别，比如人、狗、车、猫等。
接着我们需要输入make命令进行编译，这会生成darnet可执行文件。
接着我们在网上找一张图片，对这张图片（位于data文件夹下的dog_person.jpeg）进行目标检测，输入命令如下：
./darknet detect cfg/yolov3.cfg backup/yolov3.weights data/dog_person.jpeg在本地的本地环境（CPU）中，输出结果如下：
Loading weights from backup/yolov3.weights...Done!
data/dog_person.jpeg: Predicted in 19.373125 seconds.
person: 96%
person: 91%
person: 87%
person: 100%
person: 100%
person: 99%
person: 99%
dog: 100%同时，也是生成一张预测图片predictions.jpg，内容如下：
虽然漏了其中的一条狗，但丝毫不影响该模型出色的目标检测的能力！这个结果对于初次接触目标检测的我是如此地让人兴奋！
视频中的目标检测
接着是更为令人兴奋的部分。我们将在视频中进行目标检测。
由于笔者初次接触darknet，对很多内容还不是很熟悉，因此，借鉴别人已经写好的代码进行视频中的目标检测。
Python代码(detect_video.py)如下：
# import the necessary packages
import numpy as np
import argparse
import imutils
import time
import cv2
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True,
help="path to input video")
ap.add_argument("-o", "--output", required=True,
help="path to output video")
ap.add_argument("-y", "--yolo", required=True,
help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream, pointer to output video file, and
# frame dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)
# try to determine the total number of frames in the video file
try:
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
else cv2.CAP_PROP_FRAME_COUNT
total = int(vs.get(prop))
print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except Exception:
print("[INFO] could not determine # of frames in video")
print("[INFO] no approx. completion time can be provided")
total = -1
# loop over frames from the video file stream
while True:
# read the next frame from the file
(grabbed, frame) = vs.read()
# if the frame was not grabbed, then we have reached the end
# of the stream
if not grabbed:
break
# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame.shape[:2]
# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability)
# of the current object detection
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > args["confidence"]:
# scale the bounding box coordinates back relative to
# the size of the image, keeping in mind that YOLO
# actually returns the center (x, y)-coordinates of
# the bounding box followed by the boxes' width and
# height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top
# and and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates,
# confidences, and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping
# bounding boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the frame
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# check if the video writer is None
if writer is None:
# initialize our video writer
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True)
# some information on processing single frame
if total > 0:
elap = (end - start)
print("[INFO] single frame took {:.4f} seconds".format(elap))
print("[INFO] estimated total time to finish: {:.4f}".format(elap * total))
# write the output frame to disk
writer.write(frame)
# release the file pointers
print("[INFO] cleaning up...")
writer.release()
vs.release()我们把coco.names和yolov3.cfg文件都放在backup文件夹下。然后，我们再输入下面的命令（注意：jishengchong.mp4为输入视频文件，位于data文件夹下，jishengchong.avi为输出文件，里面包含目标检测的结果）：
python3 detect_video.py --input ../data/jishengchong.mp4 --output ../data/jishengchong.avi --yolo ../backup由于视频检测太费时间，因此，我在这里只给出该视频的前10秒的检测结果，如下：
总结
本次作为笔者入门目标检测的第一篇文章，希望以后也能在这个方向又一定深度的研究。
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
