#!usr/bin/python
# -*- coding: utf-8 -*-
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
cap = cv2.VideoCapture(0)
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
# 使用opencv默认的SVM分类器
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
while(1):
# get a frame
ret, frame = cap.read()
frame = imutils.resize(frame, width=min(400, frame.shape[1]))
# detect people in the image
(rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4),
padding=(8, 8), scale=1.05)
rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
# 非极大抑制 消除多余的框 找到最佳人体
pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
for (xA, yA, xB, yB) in pick:
cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
# show a frame
cv2.imshow("capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
#!usr/bin/python
# -*- coding: utf-8 -*-
#import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
def find_marker(image):
# convert the image to grayscale, blur it, and detect edges
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 35, 125)
# find the contours in the edged image and keep the largest one;
# we'll assume that this is our piece of paper in the image
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
# 求最大面积
c = max(cnts, key = cv2.contourArea)
# compute the bounding box of the of the paper region and return it
# cv2.minAreaRect() c代表点集，返回rect[0]是最小外接矩形中心点坐标，
# rect[1][0]是width，rect[1][1]是height，rect[2]是角度
return cv2.minAreaRect(c)
def find_person(image):
frame = imutils.resize(image, width=min(400, image.shape[1]))
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.05)
rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
#非极大抑制 消除多余的框 找到最佳人体位置
pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
return pick
def distance_to_camera(knownWidth, focalLength, perWidth):
# compute and return the distance from the maker to the camera
return (knownWidth * focalLength) / perWidth
# initialize the known distance from the camera to the object, which
# in this case is 24 inches
KNOWN_DISTANCE = 24.0
#KNOWN_DISTANCE = 610
# initialize the known object width, which in this case, the piece of
# paper is 11 inches wide
KNOWN_WIDTH = 11.69
KNOWN_HEIGHT = 8.27
KNOW_PERSON_HEIGHT = 70.87
#KNOWN_WIDTH = 297
#KNOWN_HEIGHT = 210
# initialize the list of images that we'll be using
IMAGE_PATHS = ["Picture1.jpg", "Picture2.jpg", "Picture3.jpg","Picture4.jpg","Picture5.jpg","Picture6.jpg","Picture7.jpg"]
# load the furst image that contains an object that is KNOWN TO BE 2 feet
# from our camera, then find the paper marker in the image, and initialize
# the focal length
image = cv2.imread(IMAGE_PATHS[0])
marker = find_marker(image)
focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH
#focalLength = 811.82
print('focalLength = ',focalLength)
camera = cv2.VideoCapture(0)
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
ya_max = 0
yb_max = 0
while camera.isOpened():
# get a frame
(grabbed, frame) = camera.read()
# 如果不能抓取到一帧，说明我们到了视频的结尾
if not grabbed:
break
frame = imutils.resize(frame, width=min(400, frame.shape[1]))
#marker = find_marker(frame)
marker = find_person(frame)
#inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])
for (xA, yA, xB, yB) in marker:
cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
ya_max = yA
yb_max = yB
pix_person_height = yb_max - ya_max
if pix_person_height == 0:
#pix_person_height = 1
continue
print (pix_person_height)
#print (pix_person_height)
inches = distance_to_camera(KNOW_PERSON_HEIGHT, focalLength, pix_person_height)
print("%.2fcm" % (inches *30.48/ 12))
# draw a bounding box around the image and display it
#box = np.int0(cv2.cv.BoxPoints(marker))
#cv2.drawContours(frame, [box], -1, (0, 255, 0), 2)
cv2.putText(frame, "%.2fcm" % (inches *30.48/ 12),(frame.shape[1] - 200, frame.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,2.0, (0, 255, 0), 3)
# show a frame
cv2.imshow("capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
#!usr/bin/python
# -*- coding: utf-8 -*-
#import the necessary packages
import numpy as np
import cv2
def find_marker(image):
# convert the image to grayscale, blur it, and detect edges
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 35, 125)
# find the contours in the edged image and keep the largest one;
# we'll assume that this is our piece of paper in the image
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
# 求最大面积
c = max(cnts, key = cv2.contourArea)
# compute the bounding box of the of the paper region and return it
# cv2.minAreaRect() c代表点集，返回rect[0]是最小外接矩形中心点坐标，
# rect[1][0]是width，rect[1][1]是height，rect[2]是角度
return cv2.minAreaRect(c)
def distance_to_camera(knownWidth, focalLength, perWidth):
# compute and return the distance from the maker to the camera
return (knownWidth * focalLength) / perWidth
# initialize the known distance from the camera to the object, which
# in this case is 24 inches
KNOWN_DISTANCE = 24.0
#KNOWN_DISTANCE = 610
# initialize the known object width, which in this case, the piece of
# paper is 11 inches wide
KNOWN_WIDTH = 11.69
KNOWN_HEIGHT = 8.27
#KNOWN_WIDTH = 297
#KNOWN_HEIGHT = 210
# initialize the list of images that we'll be using
IMAGE_PATHS = ["Picture1.jpg", "Picture2.jpg", "Picture3.jpg","Picture4.jpg","Picture5.jpg","Picture6.jpg","Picture7.jpg"]
# load the furst image that contains an object that is KNOWN TO BE 2 feet
# from our camera, then find the paper marker in the image, and initialize
# the focal length
image = cv2.imread(IMAGE_PATHS[0])
#image = cv2.imread('Picture1.jpg')
#cv2.imshow('first image',image)
marker = find_marker(image)
focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH
#focalLength = 811.82
print('focalLength = ',focalLength)
camera = cv2.VideoCapture(0)
# loop over the images
for imagePath in IMAGE_PATHS:
# load the image, find the marker in the image, then compute the
# distance to the marker from the camera
image = cv2.imread(imagePath)
marker = find_marker(image)
inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])
# draw a bounding box around the image and display it
box = np.int0(cv2.cv.BoxPoints(marker))
#% (inches *30.48/ 12)
cv2.drawContours(image, [box], -1, (0, 255, 0), 2)
cv2.putText(image, "%.2fcm" % (inches*30.48/12),
(image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,
2.0, (0, 255, 0), 3)
cv2.imshow("image", image)
#if cv2.waitKey(10)>=0:
# break
cv2.waitKey(0)
while camera.isOpened():
# get a frame
(grabbed, frame) = camera.read()
# 如果不能抓取到一帧，说明我们到了视频的结尾
if not grabbed:
break
marker = find_marker(frame)
if marker == 0:
print(marker)
continue
inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])
# draw a bounding box around the image and display it
box = np.int0(cv2.cv.BoxPoints(marker))
cv2.drawContours(frame, [box], -1, (0, 255, 0), 2)
cv2.putText(frame, "%.2fcm" % (inches *30.48/ 12),
(frame.shape[1] - 200, frame.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,
2.0, (0, 255, 0), 3)
# show a frame
cv2.imshow("capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()