使用暗网代码的人需要在 repo 中编辑 yolo_video.py 文件。`此文件有效，替换为所需的编辑
# 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"], "biscuits.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"], "yolov4-custom_best.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov4-custom.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()
print("ln",net)
ln = [ln[i - 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
# 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()`