Question

我是CV python的初学者，我正在尝试重写练习练习。一切看起来都很不错，因为我没有关于代码的任何错误，但是我对Spyder -Anaconda有问题。内核死亡，然后重新启动...别人有这个问题吗？谢谢。

代码：

import numpy as np
import argparse
import imutils
import cv2
import os
from pyimagesearch import social_distancing_config as config
from pyimagesearch.detection import detect_people
from scipy.spatial import distance as dist


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", type=str, default="",
help="/Users/macbookpro/Desktop/social-distance-detector/pedestrians.mp4")
ap.add_argument("-o", "--output", type=str, default="",
help="/Users/macbookpro/Desktop/social-distance-detector/output_2.avi")
ap.add_argument("-d", "--display", type=int, default=0,
help="whether or not output frame should be displayed")
args = vars(ap.parse_args())

# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([config.MODEL_PATH, "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([config.MODEL_PATH, "yolov3.weights"])
configPath = os.path.sep.join([config.MODEL_PATH, "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)

# check if we are going to use GPU
if config.USE_GPU:
# set CUDA as the preferable backend and target
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

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

# initialize the video stream and pointer to output video file
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(args["input"] if args["input"] else 0)
writer = None

# loop over the frames from the video 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

# resize the frame and then detect people (and only people) in it
frame = imutils.resize(frame, width=700)
results = detect_people(frame, net, ln,
    personIdx=LABELS.index("person"))

# initialize the set of indexes that violate the minimum social
# distance
violate = set()

# ensure there are *at least* two people detections (required in
# order to compute our pairwise distance maps)
if len(results) >= 2:
    # extract all centroids from the results and compute the
    # Euclidean distances between all pairs of the centroids
    centroids = np.array([r[2] for r in results])
    D = dist.cdist(centroids, centroids, metric="euclidean")

    # loop over the upper triangular of the distance matrix
    for i in range(0, D.shape[0]):
        for j in range(i + 1, D.shape[1]):
            # check to see if the distance between any two
            # centroid pairs is less than the configured number
            # of pixels
            if D[i, j] < config.MIN_DISTANCE:
                # update our violation set with the indexes of
                # the centroid pairs
                violate.add(i)
                violate.add(j)

# loop over the results
for (i, (prob, bbox, centroid)) in enumerate(results):
    # extract the bounding box and centroid coordinates, then
    # initialize the color of the annotation
    (startX, startY, endX, endY) = bbox
    (cX, cY) = centroid
    color = (0, 255, 0)

    # if the index pair exists within the violation set, then
    # update the color
    if i in violate:
        color = (0, 0, 255)

    # draw (1) a bounding box around the person and (2) the
    # centroid coordinates of the person,
    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
    cv2.circle(frame, (cX, cY), 5, color, 1)

# draw the total number of social distancing violations on the
# output frame
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
    cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)

# check to see if the output frame should be displayed to our
# screen
if args["display"] > 0:
    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

# if an output video file path has been supplied and the video
# writer has not been initialized, do so now
if args["output"] != "" and writer is None:
    # initialize our video writer
    fourcc = cv2.VideoWriter_fourcc(*"MJPG")
    writer = cv2.VideoWriter(args["output"], fourcc, 25,
        (frame.shape[1], frame.shape[0]), True)

# if the video writer is not None, write the frame to the output
# video file
if writer is not None:
    writer.write(frame)

内核死亡，并重新启动Spyder Computer Vision

0 个答案: