软件杯第二阶段

使用YOLO及Opencv实现目标检测

目录：

D:.
├─.idea
│ └─inspectionProfiles
├─darknet
│ ├─cfg
│ ├─data
│ │ └─labels
│ ├─examples
│ ├─include
│ ├─python
│ ├─scripts
│ └─src
├─images
├─output
├─videos
└─yolo-coco

# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2020/5/20 20:00
# @Author  : CuiDog
# @File    : yolo.py


#yolo.py该脚本用于图像处理

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

#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("-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 applying non-maxima suppression")
args = vars(ap.parse_args())



# class args:
#     image = 'test1.jpg'#自行替换文件
#     config = 'yolov3.cfg'
#     weights = 'yolo-coco/yolov3.weights'
#     classes = 'yolov3.txt'




#解析之后，args变量是一个包含命令行参数的键值对的字典。下面为每个标签设置随机颜色：
# 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("
")

# 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")



#上述加载所有类 LABELS，其类型是列表，保存的是类别名称，然后将随机颜色分配给每个标签  。下面设置YOLO权重和配置文件的路径，然后从磁盘加载YOLO文件：
# 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)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)





#从磁盘加载YOLO文件后，并利用OpenCV中的cv2.dnn.readNetFromDarknet函数从中读取网络文件及权重参数，此函数需要两个参数configPath 和 weightsPath，这里再次强调：OpenCV 的版本至少是3.4.2及以上才能运行此代码，因为它需要加载YOLO所需的更新的dnn模块。下面加载图像并处理：
# load our input image and grab its spatial dimensions
image = cv2.imread(args["image"])
(H, W) = image.shape[:2]

# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# construct a blob from the input image and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes and
# associated probabilities
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
                             swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()

# show timing information on YOLO
print("[INFO] YOLO took {:.6f} seconds".format(end - start))




#当blob准备好了后，我们就会通过YOLO网络进行前向传递；
#显示YOLO的推理时间；
#现在采取措施来过滤和可视化最终的结果。首先，让我们初步化一些处理过程中需要的列表：
# initialize our lists of detected bounding boxes, confidences, and
# class IDs, respectively
boxes = []
confidences = []
classIDs = []





#下面用YOLO layerOutputs中的数据填充这些列表 ：
# 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)




#过滤掉了不需要的检测结果后，我们将：

#缩放边界框坐标，以便我们可以在原始图像上正确显示它们；
#提取边界框的坐标和尺寸，YOLO返回边界框坐标形式： （centerX ，centerY ，width，height）；
#使用此信息导出边界框的左上角（x，y）坐标；
#更新boxes， confidences ，classIDs列表。
#有了这些数据后，将应用“非最大值抑制”（non-maxima suppression，nms）：
# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
    args["threshold"])




#最后在图像上绘制检测框和类文本：
# 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 image
        color = [int(c) for c in COLORS[classIDs[i]]]
        cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
        text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
                    0.5, color, 2)

# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)

截图：

# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2020/5/20 20:01
# @Author  : CuiDog
# @File    : yolo_video.py


#yolo_video.py该脚本用于视频处理

# 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("
")

# 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:
    print("[INFO] could not determine # of frames in video")
    print("[INFO] no approx. completion time can be provided")
    total = -1





#逐帧处理
# 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]


ect Detection with OpenCVPython

    # 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()

视频展示

最后就是说，把这个识别出来的视频再分割成几帧，然后需要哪帧看哪帧，界面展示出来，等于识别+动态跟踪，然后昨天视频直播，说要对识别数统计+同类物体分类，就是说人1人2人3，这个感觉有点难，网上也找不到相关代码，但计数功能可以实现，最后显示每帧的检测数多少。