def main():
windowName="Live Video Feed"
cv2.namedWindow(windowName)
cap=cv2.VideoCapture(0)
if cap.isOpened():
ret,frame=cap.read()
print(ret)
print(frame)
else:
ret=False;
#TO capture Video Recordings
while ret:
ret,frame=cap.read()
output= cv2.imshow(windowName,frame)
cv2.imshow("Gray",output)
if cv2.waitKey(1)==27:
break
cv2.destroyWindows()
cap.isrelease()
def main():
imgpath = "E:\\OCV\\Images\\standard_test_images\\lena_color_256.tif"
img = cv2.imread(imgpath)
cv2.imshow('Lena', img)
cv2.waitKey(0)
cv2.destroyWindows('Lena')
def Read_video(video_path,thresh):
videoCap = cv2.VideoCapture(video_path)
frame_count = int(videoCap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
print frame_count
count = 0
while(int(count)<frame_count):
count = count + 3
videoCap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES,count)
videoCap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT,480)
videoCap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH,640)
ret,frame = videoCap.read()
src = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
cv2.imwrite('./temp.jpg',src)
img_path = './temp.jpg'
loc,conf = get_face_loc(img_path)
if loc is None:
print 'non face'
cv2.putText(frame,'NONE_FACE',(10,30),cv2.FONT_HERSHEY_TRIPLEX,0.6,(255,255,255))
else:
roi_ary = get_roi(loc,img_path)
cv2.rectangle(frame,(int(roi_ary[0]),int(roi_ary[1])),(int(roi_ary[2]),int(roi_ary[3])),(0,0,255),1)
#do predict
try:
loc = get_phone_loc(roi_ary,thresh)
if sum(loc) !=0:
cv2.rectangle(frame,(int(loc[0]),int(loc[1])),(int(loc[2]),int(loc[3])),(0,255,0),1)
cv2.putText(frame,str(loc[-1]),(int(loc[0]),int(loc[1])),cv2.FONT_HERSHEY_TRIPLEX,0.6,(255,0,0))
except:
print 'predict_erro'
cv2.imshow('main',frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
count = frame_count
videoCap.release()
cv2.destroyWindows()
def main():
# load haar cascades model
faces = cv2.CascadeClassifier(FACE_MODEL_FILE)
eyes = cv2.CascadeClassifier(EYES_MODEL_FILE)
plates = [cv2.CascadeClassifier(p) for p in PLATE_FILES]
# connect to camera
camera = cv2.VideoCapture(0)
while not camera.isOpened():
time.sleep(0.5)
# read and show frames
progress = tqdm()
while True:
ret, frame = camera.read()
frame = process(frame, [
(faces, (255, 255, 0), dict(scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)), face_name),
(eyes, (0, 0, 255), dict(scaleFactor=1.1, minNeighbors=5, minSize=(20, 20)), eye_name),
])
# frame = process(frame, [
# (model, (0, 255, 0), dict(scaleFactor=1.1, minNeighbors=5, minSize=(20, 20)))
# for model in plates
# ])
cv2.imshow('Objects', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
progress.update()
# gracefully close
camera.release()
cv2.destroyWindows()
tqdm.close()
def main():
imgpath = "C:\\Users\\Rahul\\Pictures\\photo\\me.jpg" #image path
img = cv2.imread(imgpath) #image read
cv2.namedWindow('me', cv2.WINDOW_AUTOSIZE)
cv2.imshow('me',img)#image show
cv2.waitKey(0)
cv2.destroyWindows('me')
def main():
# all( (x1 <= x <= x + w <= x1 + w ) and (y1 <= y <= y + h <= y1 + h) for (x1,y1,h1,w1) in faces)
model = cv2.CascadeClassifier(CASCADESFILE)
modeleyes = cv2.CascadeClassifier(EYESFILE)
webcam = cv2.VideoCapture(0)
# infinite image processing loop
while True:
if not webcam.isOpened():
logging.warning('Unable to connect to camera.')
time.sleep(5)
continue
# get image from camera
ret, frame = webcam.read()
# if frame is read correctly ret is True
if not ret:
print("Can't receive frame (stream end?). Exiting ...")
break
# convert image to grayscale
grayframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces
faces = model.detectMultiScale(grayframe,
scaleFactor=3.1,
minNeighbors=5,
minSize=(30, 30))
eyes = modeleyes.detectMultiScale(grayframe,
scaleFactor=3.1,
minNeighbors=20,
minSize=(40, 40))
logging.info(f'Detected faces: {len(faces)}')
# add boxes
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
for (x, y, w, h) in eyes:
if bool(list(faces)):
if all((x1 <= x <= x + w <= x1 + w1) and (
y1 <= y <= y + h <= y1 + h1)
for (x1, y1, h1, w1) in faces):
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0),
2)
# show image
cv2.imshow('Video', frame)
# stop if user presses 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# close everything
webcam.release()
cv2.destroyWindows()
def readimg():
img = cv2.imread('test.jpg')
cv2.imshow("test", img)
k = cv2.waitKey(1111) # 0 无限期等待键盘输入，>0delay毫秒
if k == 111:
cv2.destroyAllWindows()
cv2.destroyWindows() # 删除指定窗口
cv2.imwrite("testxxx.jpg", img) # 写入到本地，
def main():
imgpath = "C:\\Users\\Rahul\\Pictures\\photo\\me.jpg" #image path
img = cv2.imread(imgpath,0) #image read # 0 for grey and 1, -1 actual
cv2.namedWindow('me', cv2.WINDOW_AUTOSIZE)
anotherfolder = "C:\\Users\\Rahul\\Pictures\\Camera Roll\\me.png"
cv2.imshow('me',img)#image show
cv2.imwrite(anotherfolder, img)
cv2.waitKey(0)
cv2.destroyWindows('me')
def detect_from_image(url):
img = url_to_image(url)
cv2.imshow('qq',img)
cv2.waitKey(0)
cv2.destroyWindows()
print(img, img.shape)
detections = detector.detectObjectsFromImage(input_image = img,
input_type='array',
output_image_path = output,
minimum_percentage_probability=600)
"""
'detectObjectsFromImage()' function is used to detect objects observable in the given image:
* input_image , which can be a filepath or image numpy array in BGR
* output_image_path (only if output_type = file) , file path to the output image that will contain the detection boxes and label, if output_type="file"
* input_type (optional) , filepath/numpy array of the image. Acceptable values are "file" and "array"
* output_type (optional) , file path/numpy array/image file stream of the image. Acceptable values are "file" and "array"
* extract_detected_objects (optional) , option to save each object detected individually as an image and return an array of the objects' image path.
* minimum_percentage_probability (optional, 30 by default) , option to set the minimum percentage probability for nominating a detected object for output.
* nms_threshold (optional, o.45 by default) , option to set the Non-maximum suppression for the detection
* display_percentage_probability (optional, True by default), option to show or hide the percentage probability of each object in the saved/returned detected image
* display_display_object_name (optional, True by default), option to show or hide the name of each object in the saved/returned detected image
* thread_safe (optional, False by default), enforce the loaded detection model works across all threads if set to true, made possible by forcing all Keras inference to run on the default graph
"""
height, width, channel = img.shape
data = {
"image": url[:8],
"labels": {
"fire": []
}
}
for detection in detections:
[x1,y1,x2, y2] = detection["box_points"]
box_height = abs(y1-y2)
box_width = abs(x1-x2)
top_ratio = round(y1/height, 2)
bot_ratio = round((height-y2)/height, 2)
l_ratio = round(x1/width, 2)
r_ratio = round((width-x2)/width, 2)
data['labels']['fire'].append({
"top": top_ratio,
"bottom": bot_ratio,
"left": l_ratio,
"right": r_ratio
})
print(data)
res = requests.post('https://apigateway.hwangsehyun.com/smartcity/sns', json=data)
def thinning(img):
size = np.size(img)
skel = np.zeros(img.shape, np.uint8)
ret, img = cv2.threshold(img, 127, 255, 0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
done = False
while (not done):
eroded = cv2.erode(img, element)
temp = cv2.dilate(eroded, element)
temp = cv2.subtract(img, temp)
skel = cv2.bitwise_or(skel, temp)
img = eroded.copy()
cvs.imwrite("thinning.png", skel)
cv2.imshow("skel", skel)
cv2.waitKey(0)
cv2.destroyWindows()
import numpy as np
import cv2
img = cv2.imread('emoji.png')
cv2.imshow('emoji',img)
cv2.waitKey(0)
cv2.destroyWindows()
def rgb2hsv(img, dim=0):
if torch.is_tensor:
log(f'Image tensor size is {img.size()}')
else:
log("This Function can only deal PyTorch Tensor!")
return img
# img = img * 0.5 + 0.5
# hue = torch.Tensor(img.shape[0], img.shape[2], img.shape[3]).to(img.device)
# hue[ img[:,2]==img.max(1)[0] ] = 4.0 + ( (img[:,0]-img[:,1]) / ( img.max(1)[0] - img.min(1)[0] + 1e-7) ) [ img[:,2]==img.max(1)[0] ]
# hue[ img[:,1]==img.max(1)[0] ] = 2.0 + ( (img[:,2]-img[:,0]) / ( img.max(1)[0] - img.min(1)[0] + 1e-7) ) [ img[:,1]==img.max(1)[0] ]
# hue[ img[:,0]==img.max(1)[0] ] = (0.0 + ( (img[:,1]-img[:,2]) / ( img.max(1)[0] - img.min(1)[0] + 1e-7) ) [ img[:,0]==img.max(1)[0] ]) % 6
# hue[img.min(1)[0]==img.max(1)[0]] = 0.0
# hue = hue/6
# saturation = ( img.max(1)[0] - img.min(1)[0] ) / ( img.max(1)[0] + 1e-7 )
# saturation[ img.max(1)[0]==0 ] = 0
# value = img.max(1)[0]
# img_hsv = torch.cat([hue.view(1,1,400,600),saturation.view(1,1,400,600),value.view(1,1,400,600)], dim=dim)
# return img_hsv
r, g, b = img.split(1, dim=dim)
gap = 1. / 6.
H = torch.zeros_like(r)
S = torch.zeros_like(r)
tensor_max = torch.max(torch.max(r, g), b)
tensor_min = torch.min(torch.min(r, g), b)
delta = tensor_max - tensor_min
V = tensor_max
g_b = (g >= b)
b_g = ~g_b
H_r_g = ((g - b) / delta) * gap
H_r_b = ((g - b) / delta) * gap + 1.
H_b = ((b - r) / delta) * gap + 2 * gap
H_g = ((r - g) / delta) * gap + 4 * gap
H_test = H.numpy().transpose(1, 2, 0)
cv2.imshow('hsv1', H_test)
cv2.waitKey(0)
cv2.destroyWindow('hsv1')
log(tensor_max == r)
log(tensor_max == g)
log(tensor_max == b)
log(g_b)
log(b_g)
H = torch.where((tensor_max == r) & g_b, H, H_r_g)
H_test = H.numpy().transpose(1, 2, 0)
cv2.imshow('hsv1', H_test)
cv2.waitKey(0)
cv2.destroyWindow('hsv1')
H = torch.where((tensor_max == r) & b_g, H, H_r_b)
H_test = H.numpy().transpose(1, 2, 0)
cv2.imshow('hsv1', H_test)
cv2.waitKey(0)
cv2.destroyWindow('hsv1')
H = torch.where(tensor_max == g, H, H_b)
H_test = H.numpy().transpose(1, 2, 0)
cv2.imshow('hsv1', H_test)
cv2.waitKey(0)
cv2.destroyWindow('hsv1')
H = torch.where(tensor_max == g, H, H_g)
H_test = H.numpy().transpose(1, 2, 0)
cv2.imshow('hsv1', H_test)
cv2.waitKey(0)
cv2.destroyWindows()
S = torch.where(tensor_max != 0, S, delta / tensor_max)
V = tensor_max
img_hsv = torch.cat([H, S, V], dim=dim)
return img_hsv
def ferme(o, titre = 'oeuil'):
cv2.destroyWindows(titre)
#!/usr/bin/env python3
import cv2
import numpy as np
#img=np.zeros([512,512,3])
#print (img)
img = cv2.imread('/home/rishabh/Downloads/superthumb.jpg', 1)
newimg = img[200:300, 230:430]
#display
#cv2.imwrite('/home/rishabh/Downloads/superthumb.jpg',newimg)
cv2.imshow('imageviewer', newimg)
cv2.waitKey(0)
#to destroy one named window
#cv2.destroyWindows('myownimage')
cv2.destroyWindows('imageviewer')
def main():
model = cv2.CascadeClassifier(CASCADESFILE)
modelesmile = cv2.CascadeClassifier(SMILEFILE)
webcam = cv2.VideoCapture(0)
# infinite image processing loop
while True:
if not webcam.isOpened():
logging.warning('Unable to connect to camera.')
time.sleep(5)
continue
# get image from camera
ret, frame = webcam.read()
# convert image to grayscale
grayframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# add blur
grayframe = cv2.GaussianBlur(grayframe, (21, 21), 0)
# detect faces
faces = model.detectMultiScale(grayframe,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
smile = modelesmile.detectMultiScale(grayframe,
scaleFactor=1.1,
minNeighbors=5,
minSize=(10, 10))
# adding non_max_suppression to avoid overlapping
faces = non_max_suppression(faces, probs=None, overlapThresh=0.65)
smile = non_max_suppression(smile, probs=None, overlapThresh=0.65)
logging.info(f'Detected faces: {len(faces)}')
# add boxes
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
for (x, y, w, h) in smile:
if bool(list(faces)):
if all((x1 <= x <= x + w <= x1 + w1) and (
y1 <= y <= y + h <= y1 + h1)
for (x1, y1, h1, w1) in faces):
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0),
2)
# show image
cv2.imshow('Video', frame)
# stop if user presses 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# close everything
webcam.release()
cv2.destroyWindows()
import cv2 as cv
img = cv.imread('lenna.png', cv.IMREAD_COLOR)
if img is None:
print("이미지 파일을 읽을 수 없습니다.")
exit(1)
roi = img[10:100, 10:100]
cv.imshow("roi", roi)
img[10:100, 10:100] = 0
cv.imshow('img', img)
cv.waitKey(0)
cv.destroyWindows()
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_dafeult.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
cap=cv2.VideoCapture()
while True:
ret, img = cap.read(0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,1.3,5)
for (x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0,255,0), 2)
cv2.imshow('img', img)
k = cv2.waitkey(30) & 0*ff
if k == 27:
break
cap.release()
cv2.destroyWindows()
import cv2
from PIL import Image
casc_path = 'C:\\Users\\user\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_eye.xml'
faceCascade = cv2.CascadeClassifier(casc_path)
imagename = 'Nikola_Tesla.jpg'
image = cv2.imread(imagename)
faces = faceCascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors = 5, minSize = (30,30), \
flags = cv2.CASCADE_SCALE_IMAGE)
count = 1
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (128, 255, 0), 2)
filename = 'face' + str(count) + '.jpg'
image1 = Image.open(imagename)
image2 = image1.crop((x, y, x + w, y + h))
image3 = image2.resize((200, 200), Image.ANTIALIAS)
image3.save(filename)
count += 1
cv2.namedWindow('facedetect')
cv2.imshow('facedetect', image)
cv2.waitKey(0)
cv2.destroyWindows('facedetect')
return cv2.bitwise_and(fark1, fark2)
esik_deger = 140000
kamera = cv2.VideoCapture(0)
pencereIsmi = "Hareket Algilayici"
cv2.namedWindow(pencereIsmi)
t_eksi = cv2.cvtColor(kamera.read()[1], cv2.COLOR_BGR2GRAY)
t = cv2.cvtColor(kamera.read()[1], cv2.COLOR_BGR2GRAY)
t_arti = cv2.cvtColor(kamera.read()[1], cv2.COLOR_BGR2GRAY)
zamanKontrol = datetime.now().strftime('%Ss')
while True:
cv2.imshow(pencereIsmi, kamera.read()[1])
if cv2.countNonZero(farkImaj(
t_eksi, t, t_arti)) > esik_deger and zamanKontrol != datetime.now(
).strftime('%Ss'):
fark_resim = kamera.read()[1]
cv2.imwrite(datetime.now().strftime('%Y%m%d_%Hh%Mm%Ss%f') +
'.jpg'.fark_resim)
zamanKontrol = datetime.now().strftime('%Ss')
t_eksi = t
t = t_arti
t_arti = cv2.cvtColor(kamera.read()[1], cv2.COLOR_BGR2GRAY)
key = cv2.waitKey(10)
if key == 27:
cv2.destroyWindows(pencereIsmi)
break
