开源 Python OpenCV 小项目
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1. Drowsiness Detector 睡意检测
环境配置
import cv2 # opencv-python
import dlib #
from scipy.spatial import distance
若pip install报错，需要先安装boost和cmake
pip install boost
pip install cmake
pip install dlib
def calculate_EAR(eye):
A = distance.euclidean(eye[1], eye[5])
B = distance.euclidean(eye[2], eye[4])
C = distance.euclidean(eye[0], eye[3])
ear_aspect_ratio = (A+B)/(2.0*C)
return ear_aspect_ratio
cap = cv2.VideoCapture(0)
hog_face_detector = dlib.get_frontal_face_detector()
dlib_facelandmark = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = hog_face_detector(gray)
for face in faces:
face_landmarks = dlib_facelandmark(gray, face)
leftEye = []
rightEye = []
for n in range(36,42):
x = face_landmarks.part(n).x
y = face_landmarks.part(n).y
leftEye.append((x,y))
next_point = n+1
if n == 41:
next_point = 36
x2 = face_landmarks.part(next_point).x
y2 = face_landmarks.part(next_point).y
cv2.line(frame,(x,y),(x2,y2),(0,255,0),1)
for n in range(42,48):
x = face_landmarks.part(n).x
y = face_landmarks.part(n).y
rightEye.append((x,y))
next_point = n+1
if n == 47:
next_point = 42
x2 = face_landmarks.part(next_point).x
y2 = face_landmarks.part(next_point).y
cv2.line(frame,(x,y),(x2,y2),(0,255,0),1)
left_ear = calculate_EAR(leftEye)
right_ear = calculate_EAR(rightEye)
EAR = (left_ear+right_ear)/2
EAR = round(EAR,2)
if EAR<0.26:
cv2.putText(frame,"DROWSY",(20,100),
cv2.FONT_HERSHEY_SIMPLEX,3,(0,0,255),4)
cv2.putText(frame,"Are you Sleepy?",(20,400),
cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),4)
print("Drowsy")
print(EAR)
cv2.imshow("Are you Sleepy", frame)
key = cv2.waitKey(1)
if key == 27:
break
cap.release()
cv2.destroyAllWindows()
2. Object Tracking 目标跟踪
pip install opencv-contrib-python
import cv2
tracker = cv2.TrackerKCF_create()
video = cv2.VideoCapture(1)
while True:
k,frame = video.read()
cv2.imshow("Tracking",frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
bbox = cv2.selectROI(frame, False)
ok = tracker.init(frame, bbox)
cv2.destroyWindow("ROI selector")
while True:
ok, frame = video.read()
ok, bbox = tracker.update(frame)
if ok:
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]),
int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (0,0,255), 2, 2)
cv2.imshow("Tracking", frame)
k = cv2.waitKey(1) & 0xff
if k == 27 : break
3. lane detection 车道线检测
import cv2
import numpy as np
def canny(img):
if img is None:
cap.release()
cv2.destroyAllWindows()
exit()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
kernel = 5
blur = cv2.GaussianBlur(gray,(kernel, kernel),0)
canny = cv2.Canny(gray, 50, 150)
return canny
def region_of_interest(canny):
height = canny.shape[0]
width = canny.shape[1]
mask = np.zeros_like(canny)
triangle = np.array([[
(200, height),
(800, 350),
(1200, height),]], np.int32)
cv2.fillPoly(mask, triangle, 255)
masked_image = cv2.bitwise_and(canny, mask)
return masked_image
def houghLines(cropped_canny):
return cv2.HoughLinesP(cropped_canny, 2, np.pi/180, 100,
np.array([]), minLineLength=40, maxLineGap=5)
def addWeighted(frame, line_image):
return cv2.addWeighted(frame, 0.8, line_image, 1, 1)
def display_lines(img,lines):
line_image = np.zeros_like(img)
if lines is not None:
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(line_image,(x1,y1),(x2,y2),(0,0,255),10)
return line_image
def make_points(image, line):
slope, intercept = line
y1 = int(image.shape[0])
y2 = int(y1*3.0/5)
x1 = int((y1 - intercept)/slope)
x2 = int((y2 - intercept)/slope)
return [[x1, y1, x2, y2]]
def average_slope_intercept(image, lines):
left_fit = []
right_fit = []
if lines is None:
return None
for line in lines:
for x1, y1, x2, y2 in line:
fit = np.polyfit((x1,x2), (y1,y2), 1)
slope = fit[0]
intercept = fit[1]
if slope < 0:
left_fit.append((slope, intercept))
else:
right_fit.append((slope, intercept))
left_fit_average = np.average(left_fit, axis=0)
right_fit_average = np.average(right_fit, axis=0)
left_line = make_points(image, left_fit_average)
right_line = make_points(image, right_fit_average)
averaged_lines = [left_line, right_line]
return averaged_lines
cap = cv2.VideoCapture("test1.mp4")
while(cap.isOpened()):
_, frame = cap.read()
canny_image = canny(frame)
cropped_canny = region_of_interest(canny_image)
# cv2.imshow("cropped_canny",cropped_canny)
lines = houghLines(cropped_canny)
averaged_lines = average_slope_intercept(frame, lines)
line_image = display_lines(frame, averaged_lines)
combo_image = addWeighted(frame, line_image)
cv2.imshow("result", combo_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
5. background removal 背景移除
import cv2
import numpy as np
import sys
def resize(dst,img):
width = img.shape[1]
height = img.shape[0]
dim = (width, height)
resized = cv2.resize(dst, dim, interpolation = cv2.INTER_AREA)
return resized
video = cv2.VideoCapture(1)
oceanVideo = cv2.VideoCapture("ocean.mp4")
success, ref_img = video.read()
flag = 0
while(1):
success, img = video.read()
success2, bg = oceanVideo.read()
bg = resize(bg,ref_img)
if flag==0:
ref_img = img
# create a mask
diff1=cv2.subtract(img,ref_img)
diff2=cv2.subtract(ref_img,img)
diff = diff1+diff2
diff[abs(diff)<13.0]=0
gray = cv2.cvtColor(diff.astype(np.uint8), cv2.COLOR_BGR2GRAY)
gray[np.abs(gray) < 10] = 0
fgmask = gray.astype(np.uint8)
fgmask[fgmask>0]=255
#invert the mask
fgmask_inv = cv2.bitwise_not(fgmask)
#use the masks to extract the relevant parts from FG and BG
fgimg = cv2.bitwise_and(img,img,mask = fgmask)
bgimg = cv2.bitwise_and(bg,bg,mask = fgmask_inv)
#combine both the BG and the FG images
dst = cv2.add(bgimg,fgimg)
cv2.imshow('Background Removal',dst)
key = cv2.waitKey(5) & 0xFF
if ord('q') == key:
break
elif ord('d') == key:
flag = 1
print("Background Captured")
elif ord('r') == key:
flag = 0
print("Ready to Capture new Background")
cv2.destroyAllWindows()
video.release()
6. YOLO 目标检测
需要准备权重、配置文件和coco类别文件，YOLO
import cv2
import numpy as np
yolo = cv2.dnn.readNet("./CV_101/yolov3.weights", "./CV_101/yolov3.cfg")
classes = []
with open("./CV_101/coco.names", "r") as file:
classes = [line.strip() for line in file.readlines()]
layer_names = yolo.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in yolo.getUnconnectedOutLayers()]
colorRed = (0,0,255)
colorGreen = (0,255,0)
# #Loading Images
name = "./CV_101/c1.png"
img = cv2.imread(name)
height, width, channels = img.shape
# # Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
yolo.setInput(blob)
outputs = yolo.forward(output_layers)
class_ids = []
confidences = []
boxes = []
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
cv2.rectangle(img, (x, y), (x + w, y + h), colorGreen, 3)
cv2.putText(img, label, (x, y + 10), cv2.FONT_HERSHEY_PLAIN, 8, colorRed, 8)
cv2.imshow("Image", img)
# cv2.imwrite("output.jpg",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
7. plate detection 车牌检测
import cv2
import pytesseract
# Read the image file
image = cv2.imread('car0.JPG')
# Convert to Grayscale Image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#Canny Edge Detection
canny_edge = cv2.Canny(gray_image, 170, 200)
# Find contours based on Edges
contours, new = cv2.findContours(canny_edge.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours=sorted(contours, key = cv2.contourArea, reverse = True)[:30]
# Initialize license Plate contour and x,y coordinates
contour_with_license_plate = None
license_plate = None
x = None
y = None
w = None
h 在 Python 多处理进程中运行较慢的 OpenCV 代码片段
Python有黄金屋，OpenCV有颜如玉，人脸融合算法开源，堪比整容！