行人与车辆检测计数&人脸识别
（未完）
人脸识别并计数1
from PIL import Image
import face_recognition
import cv2
# Load the jpg file into a numpy array
pic=("2.jpg")
image = face_recognition.load_image_file(pic)
img=cv2.imread(pic)
# Find all the faces in the image using the default HOG-based model.
# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
# See also: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)
print("I found {} face(s) in this photograph.".format(len(face_locations)))
for face_location in face_locations:
# Print the location of each face in this image
top, right, bottom, left = face_location
print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
img = cv2.rectangle(img, (left,top), (right,bottom), (0, 255, 0), 2) # 绘制矩形
cv2.imshow("show", img)
# You can access the actual face itself like this:
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
cv2.waitKey(0)
#pil_image.show()
人脸识别并计数2
import cv2
img=cv2.imread("2.jpg")
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转化为灰度图
faces = face_cascade.detectMultiScale(gray, 1.04, 5) # 返回人脸矩形数组
#faces = face_cascade.detectMultiScale(gray, 1.03, 2, cv2.CASCADE_SCALE_IMAGE, (50, 50), (100, 100)) 分别是 图片、检测缩放比例、检测次数、
i= 0
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2) #绘制矩形
i=i+ 1#统计
#cv2.imwrite( 'd: \\ test.jpg' , img)
img = cv2.resize(img , ( 1200 ,800 ))
print (i)
cv2.imshow("show", img )
cv2.waitKey( 0 )
车辆行人检测1
import cv2
import dlib
import time
import threading
import math
carCascade = cv2.CascadeClassifier('myhaar.xml')
video = cv2.VideoCapture(r'cars.mp4')
WIDTH = 1280
HEIGHT = 720
def estimateSpeed(location1, location2):
d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
# ppm = location2[2] / carWidht
ppm = 8.8
d_meters = d_pixels / ppm
# print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
fps = 18
speed = d_meters * fps * 3.6
return speed
def trackMultipleObjects():
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 0
js=0
zs=0
carTracker = {}
carNumbers = {}
carLocation1 = {}
carLocation2 = {}
speed = [None] * 1000
# Write output to video file
out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (WIDTH, HEIGHT))
while True:
start_time = time.time()
rc, image = video.read()
if type(image) == type(None):
break
image = cv2.resize(image, (WIDTH, HEIGHT))
resultImage = image.copy()
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(image)
if trackingQuality < 7:
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
print('Removing carID ' + str(carID) + ' from list of trackers.')
print('Removing carID ' + str(carID) + ' previous location.')
print('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
js=js+1
if not (frameCounter % 10):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
for (_x, _y, _w, _h) in cars:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (
x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
currentCarID = currentCarID + 1
zs=currentCarID-js
# cv2.line(resultImage,(0,480),(1280,480),(255,0,0),5)
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4)
center_x = t_x + t_w // 2
center_y = t_y + t_h // 2
# rects.append((t_x, t_y, t_w, t_h))
cv2.circle(resultImage, (center_x, center_y), 8, (0, 255, 255), 1)
# speed estimation
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
if not (end_time == start_time):
fps = 1.0 / (end_time - start_time)
# cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
for i in carLocation1.keys():
if frameCounter % 1 == 0:
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
carLocation1[i] = [x2, y2, w2, h2]
# print 'new previous location: ' + str(carLocation1[i])
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
if (speed[i] == None or speed[i] == 0) and y1 >= 275 and y1 <= 285:
speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2])
# if y1 > 275 and y1 < 285:
if speed[i] != None and y1 >= 180:
cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1 / 2), int(y1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
info = [
("tracker", zs)
]
for (i, (k, v)) in enumerate(info):
text = "{}: {} people".format(k, v)
cv2.putText(resultImage, text, (670, HEIGHT - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
# print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
# else:
# cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# print ('CarID ' + str(i) + ' Location1: ' + str(carLocation1[i]) + ' Location2: ' + str(carLocation2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
cv2.imshow('result', resultImage)
# Write the frame into the file 'output.avi'
# out.write(resultImage)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
if __name__ == '__main__':
trackMultipleObjects()
行人检测计数2
import cv2
import dlib
import time
import threading
import numpy as np
# from imutils.object_detection import non_max_suppression
import math
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
carCascade = cv2.CascadeClassifier('myhaar.xml')
video = cv2.VideoCapture('zuqiu.flv')
def estimateSpeed(location1, location2):
d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
# ppm = location2[2] / carWidht
ppm = 8.8
d_meters = d_pixels / ppm
# print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
fps = 18
speed = d_meters * fps * 3.6
return speed
def trackMultipleObjects():
out = None
counter_right = []
counter_moto = []
counter_wrong = []
counter_wrong_detect = []
rects = []
js=0
line_pass_left = np.array([[115, 200], [115, 320]])
line_pass_right = np.array([[930, 200], [930, 320]])
mask_people = np.array([line_pass_left[0], line_pass_left[1], line_pass_right[1], line_pass_right[0]])
# line_down = [(0, 360), (800, 360)]
# rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 0
carTracker = {}
carNumbers = {}
carLocation1 = {}
carLocation2 = {}
speed = [None] * 1000
HEIGHT = 720
WIDTH = 1280
# EXIT_COLOR = (66, 183, 42)
# Write output to video file
out = cv2.VideoWriter('project_output_haar_and_svm1.avi', cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 10,
(WIDTH, HEIGHT))
while True:
start_time = time.time()
rc, image = video.read()
if type(image) == type(None):
break
image = cv2.resize(image, (WIDTH, HEIGHT))
resultImage = image.copy()
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(image)
if trackingQuality < 7:
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
print('Removing carID ' + str(carID) + ' from list of trackers.')
print('Removing carID ' + str(carID) + ' previous location.')
print('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
js = js+1#减少追踪数量
# detecting
if not (frameCounter % 10):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(rects, weights) = hog.detectMultiScale(gray, winStride=(4, 4),
padding=(8, 8), scale=1.01)
for (_x, _y, _w, _h) in rects:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (
x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
# if new car exists
if matchCarID is None:
print('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, x + w, y + h]
currentCarID = currentCarID + 1
zs=currentCarID-js
print(zs)
# cv2.line(resultImage, line_pass_right[0], line_pass_right[1], (255, 255, 0), 5)
# cv2.line(resultImage, line_pass_left[0], line_pass_left[1], (255, 255, 0), 5)
# cv2.line(resultImage, line_down[0], line_down[1], (255, 0, 0), 5)
#
# tracking
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
center_x = t_x + t_w//2
center_y = t_y + t_h//2
#rects.append((t_x, t_y, t_w, t_h))
cv2.circle(resultImage, (center_x, center_y),5, (0, 255, 255), 3)
cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), (0, 255, 0), 4)
# ---------------------------------------------------------------------
# t_x = int(trackedPosition.left())
# t_y = int(trackedPosition.top())
# t_w = int(trackedPosition.width())
# t_h = int(trackedPosition.height())
#
# cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h),(0, 255, 0), 4)
# ------------------------------------------------------------------
"""
if ((line_pass_left[0][0] <= center_x <= line_pass_left[1][0] + 40) and ( line_pass_left[0][1] <= center_y <= line_pass_left[1][1])) or ((line_pass_right[0][0] - 10 <= center_x <= line_pass_right[1][0]) and ( line_pass_right[0][1] <= center_y <= line_pass_right[1][1])):
counter_right.append(carID)
cv2.putText(resultImage, "Pedestrian", ( center_x - 10, center_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
elif (line_down[0][0] <= center_x <= line_down[1][0]) and (line_down[0][1] - 20 <= center_y <= line_down[0][1] + 20):
counter_moto.append(carID)
cv2.putText(resultImage, "moto", (center_x - 10, center_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 250, 250), 2)
else:
counter_wrong.append(carID)
cv2.putText(resultImage, "Pedestrian", (center_x - 10, center_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# speed estimation
carLocation2[carID] = [t_x, t_y, t_w, t_h]
info = [
("walking in right place", len(set(counter_right))),
("moto", len(set(counter_moto))),
("walking in wrong place", len(set(counter_wrong))),
]
for (i, (k, v)) in enumerate(info):
text = "{}: {} people".format(k, v)
cv2.putText(resultImage, text, (670, HEIGHT - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
"""
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
if not (end_time == start_time):
fps = 1.0 / (end_time - start_time)
# cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
for i in carLocation1.keys():
if frameCounter % 1 == 0:
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
carLocation1[i] = [x2, y2, w2, h2]
# print 'new previous location: ' + str(carLocation1[i])
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
if (speed[i] == None or speed[i] == 0):
speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2])
# if y1 > 275 and y1 < 285:
if (33 <= speed[i] <= 45) and y1 >= 100:
cv2.circle(resultImage, (x1 + w1 // 2, y1 + h1 // 2), 4, (0, 255, 255), -1)
cv2.putText(resultImage, "P:" + str(int(speed[i])) + " km/hr", (int(x1 + w1 / 2), int(y1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
counter_right.append(i)
if speed[i] > 45:
cv2.circle(resultImage, (x1 + w1 // 2, y1 + h1 // 2), 4, (0, 255, 255), -1)
cv2.putText(resultImage, "Moto" + str(int(speed[i])) + "km/hr",
(int(x1 + w1 / 2), int(y1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)
counter_moto.append(i)
info = [
("walking in right place", len(set(counter_right))),
("moto", len(set(counter_moto))),
("tracker",zs)
]
for (i, (k, v)) in enumerate(info):
text = "{}: {} people".format(k, v)
cv2.putText(resultImage, text, (670, HEIGHT - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
# print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
# else:
# cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# print ('CarID ' + str(i) + ' Location1: ' + str(carLocation1[i]) + ' Location2: ' + str(carLocation2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
cv2.imshow('result', resultImage)
# Write the frame into the file 'output.avi'
# out.write(resultImage)
out.write(resultImage)
if cv2.waitKey(33) == 27:
break
# # check to see if we need to release the video writer pointer
# if out is not None:
# out.release()
#
# # release the video file pointer
# video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
trackMultipleObjects()
借鉴大佬代码Kewei_LI_ECN