def show_pic(self):
# 读取一帧
success, frame = self.cap.read()
if success:
while True:
# 发现在视频帧所有的脸和face_enqcodings
face_locations = face_recognition.face_locations(frame)
face_encodings = face_recognition.face_encodings(frame, face_locations)
# 在这个视频帧中循环遍历每个人脸
for (top, right, bottom, left), face_encoding in zip(
face_locations, face_encodings):
# 画出一个框，框住脸
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# 画出一个带名字的标签，放在框下
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255),
cv2.filled)
font = cv2.font_hershey_duplex
cv2.puttext(frame, 'face', (left + 6, bottom - 6), font, 1.0,
(255, 255, 255), 1)
# 显示结果图像
cv2.imshow('video', frame)
if cv2.waitkey(1) & 0xff == ord('q'):
break
self.cap.release()
cv2.destroyAllWindows()
def get_frame(self):
# Camera.last_access = time.time()
# self.__init__()
success, image = self.video.read() #record from camera
cv2.flip(image, 180) #camera rotate 180 degree
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = self.faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 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:
cv.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh),
(0, 0, 255), 2)
face_count = len(faces)
inType = "Found %d faces." % face_count
print face_count
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.puttext(image, inType, (10, 500), font, 4, (255, 255, 255), 2,
cv2.LINE_AA)
ret, jpeg = cv2.imencode('.jpg', image)
return jpeg.tostring()
def gen():
""" video streaming Geneatorr function."""
cap = cv2.VideoCapture('test.mp4')
while (cap.isOpened()):
#capture frame-by-frame
ret, frame = cap.read()
if not ret:
frame = cv2.VideoCapture("test.mp4")
continue
if ret:
image = cv2.resize(frame, (0, 0), None, 1, 1)
gray = cv2.cvtcolor(image, cv2.COLOR_BGR2GRAY)
fgmask = sub.apply(gray)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
dilation = cv2.dilate(opening, kernel)
retvalbin, bins = cv2.threshold(dilation, 220, 255,
cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
minarea = 400
maxarea = 50000
for i in range(len(contours)):
if hierarchy[0, i, 3] == -1:
area = cv2.contourArea(contours[i])
if minarea < area < maxarea:
cnt = contours[i]
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(image, (x, y), (x + w, y + h),
(0, 255, 0), 2)
cv2.puttext(image,
str(cx) + "," + str(cy),
(cx + 10, cy + 10),
cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 0, 255),
1)
cv2.drawMarker(image, (cx, cy), (0, 255, 255),
cv2.MARKER_CROSS,
markerSize=8,
thickness=3,
line_type=cv2.LINE_8)
frame = cv2.imencode('.jpg', image)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
time.sleep(0.1)
key = cv2.waitKey(20)
if key == 27:
break
def visulize_result(image_path,result_path,save_path):
results = json.load(open(result_path))
im_bbox = {}
for res in results:
name = res['name']
bbox = res['bbox']
category = res['category']
if not name in im_bbox.keys():
im_bbox[name] = [bbox,category]
for im_name in im_bbox.keys():
img_path = osp.join(image_path,im_name)
image = cv2.imread(img_path)
for ann in im_bbox[im_name]:
bbox = ann[0]
cat = ann[1]
image = cv2.rectangle(image,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),3)
image = cv2.puttext(image,str(cat),(bbox[0],bbox[1]),cv2.FONT_HERSHEY_SIMPLEX,10,(0,0,255),3)
img_save = osp.join(save_path,im_name)
cv2.imwrite(img_save,image)
#storing the whole directory in a list
images_list = glob(os.path.join(images_dir, "*.jpg"))
for img_filename in images_list:
img = cv2.imread(img_filename)
roi = img[269: 795, 537: 1416]
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
imgray = cv2.GaussianBlur(imgray, (7,7), 0)
ret, thresh = cv2.threshold(imgray, 4, 255, cv2.THRESH_BINARY_INV)
contours, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
noOfContours = str(len(contours))
if noOfContours == 0:
cv2.puttext("Warning")
else:
print("number of contours = "+noOfContours)
for cnt in contours:
(x, y, w, h) = cv2.boundingRect(cnt)
cv2.rectangle(roi, (x,y), (x + w , y+ h), (255, 0, 0), 2)
#cv2.imshow('image',img)
cv2.imshow('ROI',roi)
cv2.waitKey(0)
cv2.destroyAllWindows()
def saveResult(img_file,
img,
boxes,
dirname='./result/',
verticals=None,
texts=None):
""" save text detection result one by one
Args:
img_file (str): image file name
img (array): raw image context
boxes (array): array of result file
Shape: [num_detections, 4] for BB output / [num_detections, 4] for QUAD output
Return:
None
"""
img = np.array(img)
# make result file list
filename, file_ext = os.path.splitext(os.path.basename(img_file))
# result directory
res_file = dirname + "res_" + filename + '.txt'
res_img_file = dirname + "res_" + filename + '.jpg'
if not os.path.isdir(dirname):
os.mkdir(dirname)
with open(res_file, 'w') as f:
for i, box in enumerate(boxes):
poly = np.array(box).astype(np.int32).reshape((-1))
strResult = ','.join([str(p) for p in poly]) + '\r\n'
f.write(strResult)
poly = poly.reshape(-1, 2)
cv2.polylines(img, [poly.reshape((-1, 1, 2))],
True,
color=(0, 0, 255),
thickness=2)
cv2.puttext()
ptColor = (0, 255, 255)
if verticals is not None:
if verticals[i]:
ptColor = (255, 0, 0)
if texts is not None:
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
cv2.putText(img,
"{}".format(texts[i]),
(poly[0][0] + 1, poly[0][1] + 1),
font,
font_scale, (0, 0, 0),
thickness=1)
cv2.putText(img,
"{}".format(texts[i]),
tuple(poly[0]),
font,
font_scale, (0, 255, 255),
thickness=1)
# Save result image
cv2.imwrite(res_img_file, img)
while True:
img_url = urllib.request.urlopen(URL)
image = np.array(bytearray(img_url.read()), np.uint8)
frame = cv2.imdecode(image, -1)
faces = classifier.detectMultiScale(frame, 1.5, 5)
if faces is not None:
for x, y, w, h in faces:
face_image = frame[y:y + h, x:x + w].copy()
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 5)
if len(data) <= 100:
data.append(face_image)
else:
cv2.puttext(frame, 'complete', (200, 200),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2)
cv2.imshow('capture', frame)
if cv2.waitKey(1) == ord('q'):
break
cv2.destroyAllWindows()
name = input("enter name: ")
c = 0
for i in data:
cv2.imwrite("images/" + name + '_' + str(c) + '.jpg', i)
c = c + 1
for i in range(0, 12):
plt.imshow(data[i])
plt.show()
import cv2
import numpy as np
faceDetect = cv2.CascadeClassifier(
'opencv-3.3.0/data/haarcascades/haarcascade_frontalface_default.xml')
cam = cv2.VideoCapture(0)
rec = cv2.LBPHFaceRecogniser()
rec.load(
'/home/pi/Desktop/Raspberry-Face-Recognition-master/trainer/trainer.yml')
id = 0
font = cv2.FONT_HERSHEY_SIMPLEX
while (TRUE):
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceDetect.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
id, conf = rec.predict(gray[y:y + h, x:x + w])
if (id == 1):
print('neeraj')
elif (id == 2):
cv2.puttext(cv2.cv.fromarray(img), str(id), (x, y + h), font, 255)
cv2.imshow('Face', img)
if (cv2.waitKey(1) == ord('q')):
break
cam.release()
cv2.destroyAllWindows()