首先导入相应的包同时设置所需要的参数：
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())参数含义：
-- image：输入图像的路径；
-- yolo ：YOLO文件路径，脚本将加载所需的YOLO文件，以便在图像上执行对象检测；
-- confidence ：过滤弱检测的最小概率，默认值设置为0.5，但该值也可以随意设置；
-- threshold ：非最大值抑制阈值，默认值设置为 0.3，可以在此处阅读有关非最大值抑制的设置标注颜色：
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "object_detection_classes_yolov3.txt"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(666)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")设置YOLO权重和配置文件的路径，并且加载网络：
# 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)加载图片并且进行处理：
# 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))在该代码中：
- 加载输入 图像并获得其尺寸；
- 确定YOLO模型中的输出图层名称；
- 从图像构造一个 blob结构；
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 = []这些列表包括：
boxes：对象的边界框。
confidences ：YOLO分配给对象的置信度值，较低的置信度值表示该对象可能不是网络认为的对象。上面的命令行参数中将过滤掉不大于 0.5阈值的对象。
classIDs：检测到的对象的类标签。下面用YOLOlayerOutputs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)在这个块中：
- 循环遍历每个layerOutputs；
- 循环每个detection中output；
- 提取 classID和 confidence；
- 使用confidence滤除弱检测；
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, 1, lineType=cv2.LINE_AA)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, color, 1, lineType=cv2.LINE_AA)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)识别红酒和杯子：
识别人物：
