def show_camera(self):
flag,self.image = self.cap.read()
show = cv2.resize(self.image,(640,480))
show = cv2.cvColor(show,cv2.color_RER2RGB)
show = cv2.cvColor(show.data,cv2.color_RER2RGB)
showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], QtGui.QImage.Format_RGB888)
self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage))
showImage = QtGui.QImage(show.data,show.shape[1],show.shape[0],QtGui.QImage.Format)
def blend_transparent(face_img, overlay_t_img):
overlay_img = overlay_t_img[:, :, :3]
overlay_mask = overlay_t_img[:, :, 3:]
background_mask = 255 - overlay_mask
overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
background_mask = cv2.cvColor(background_mask, cv2.COLOR_GRAY2BGR)
face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))
return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0,
0.0))
def process_video(content_path, style_path, outfile):
j = 0
video, videoWriter = load_video(content_path, style_path, outfile)
while (video.isOpened()):
j = j + 1
ret, frame = video.read()
if not ret:
break
if j % 1 == False:
# 对每一帧进行风格化。
style = Image.open(style_path)
content = Image.fromarray(cv2.cvColor(frame, cv2.COLOR_BGR2RGB))
output = image_process(frame, style)
# 对风格化后的结果进行额外处理，以存储到视频中
save_frame(output, videoWriter)
time.sleep(conf["camera_warmup_time"]
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
print('[INFO] poly_watch started !!')
# capture farmes from the camera
for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
#grab the raw NumPy array of the image and init timestamp and text
frame = f.array
timestamp = datetime.datetime.now()
text = "Unoccupied"
#Computer Vision!
# resize the frame, convert to grayscale, and blur
gray = cv2.cvColor(frame, cv.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, tuple(conf['blur_size']), 0)
#if the avg frame is None, init
if avg is None:
print("[INFO] starting background model...")
avg = gray.copy().astype("float")
rawCapture.truncate(0)
continue
#accumulate the weighted average between the current frame and
#previous frames, then computer the diff between the current
#fram and running average
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
cv2.accumulateWeighted(gray, avg, 0.5)
#intent :
#Author :Michael Jack hu
#start date : 2018/10/8
#File : 人脸识别2.py
#Software : PyCharm
#finish date :
'''
# 在识别的图片上面添加人脸识别：重点注意 需要添加模型
# 1.导入库
import cv2
# 2.加载图片
img = cv2.imread('D:\04.jpg')
# 3.加载人脸模型
face = cv2.Case("D:\timg.jpg")
# 4.调整图片灰度
gray = cv2.cvColor(img, cv2.COLOR_RGB2GRAY)
# 5.检查人脸
faces = face.detectMultiScale(gray)
# 6.标记人脸
for (x, y, w, h) in faces:
#里面有四个参数 1，写图片 2，坐标原点 3，识别大小 4，颜色RGB5，线宽
cv2.rectang(img, (x, y), (x + w, y + h), (0, 255, 0), 10)
# 7.创建窗口
cv2.nameWindow('james 窗口')
# 8.显示图片
cv2.imshow('jiaqi', img)
# 9.暂停窗口
cv2.waitKey(0)
# 10.关闭窗口
cv2.destroyAllWindows()
hist_scale = val
cv2.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
try:
fn = sys.argv[1]
except:
fn = 0
cam = video.create_capture(fn, fallback=' ')
while True:
flag, frame = cam.read()
cv2.imshow('camera', frame)
small = cv2.pyrDown(frame)
hsv = cv2.cvColor(frame, cv2.COLOR_BGR2HSV)
dark = hsv[:, :, 2] < 32
hsv[dark] = 0
h = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
h = np.clip(h * 0.005 * hist_scale, 0, 1)
vis = hsv_map * h[:, :, np.newaxis] / 255.0
cv2.imshow('hist', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
import numpy as np
from PIL import ImageGrab
import cv2
fourcc = cv2.VideoWriter_fourcc(*"XVID")
size = (ImageGrab.grab()).size
output = cv2.VideoWriter("output.mp4", fourcc, 5.0, size)
while True:
img = np.array(ImageGrab.grab())
frame = cv2.cvColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow("Screen", frame)
output.write(img)
if cv2.waitkey(1) == 27:
break
output.release()
cv2.destroyAllWindows