python - Opencv 屏幕记录作为视频处理的输入
问题描述
试图让屏幕记录充当我的代码的视频流输入，目前正在尝试使用 mss 循环来捕获屏幕，这是有效的
import time
import cv2
import mss
import numpy
with mss.mss() as sct:
monitor = {"top": 40, "left": 0, "width": 800, "height": 640}
while "Screen capturing":
last_time = time.time()
img = numpy.array(sct.grab(monitor))
cv2.imshow("OpenCV/Numpy normal", img)
# print("fps: {}".format(1 / (time.time() - last_time)))
# Press "q" to quit
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
这将显示一个 cv2imshow 窗口，该窗口更新并显示我的屏幕的一部分，因此部分工作。我想将此设置为以下函数的输入。
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import numpy as np
import cv2
import pickle
import glob
from tracker import tracker
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.gridspec as gridspec
import mss
import time
def process_image(img):
#undistort the image
bot_width = .80 # percentage of bottom trapezoidal height 76
mid_width = .20 # percentage of mid trapezoidal height 08
height_pct = .45 # percentage of trapezoidal height 62
bottom_trim= .75 # percentage from top to bottom avoiding the hood of the car
#img = cv2.undistort(img,mtx,dist,None,mtx)
src = np.float32([[img.shape[1]*(0.5-mid_width/2), img.shape[0]*height_pct],[img.shape[1]*(0.5+mid_width/2),img.shape[0]*height_pct],[img.shape[1]*(0.5+bot_width/2), img.shape[0]*bottom_trim],[img.shape[1]*(0.5-bot_width/2), img.shape[0]*bottom_trim]])
warptrap = np.copy(img)
cv2.line(warptrap, (int(src[0][0]), int(src[0][1])), (int(src[1][0]), int(src[1][1])), [255,0,0], 10, cv2.LINE_AA)
cv2.line(warptrap, (int(src[1][0]), int(src[1][1])), (int(src[2][0]), int(src[2][1])), [255,0,0], 10, cv2.LINE_AA)
cv2.line(warptrap, (int(src[2][0]), int(src[2][1])), (int(src[3][0]), int(src[3][1])), [255,0,0], 10, cv2.LINE_AA)
cv2.line(warptrap, (int(src[3][0]), int(src[3][1])), (int(src[0][0]), int(src[0][1])), [255,0,0], 10, cv2.LINE_AA)
#pass image thru the pipeline
#preprocessImage = np.zeros_like(img[:,:,0])
#gradx = abs_sobel_thresh(img, orient='x',thresh_min=20, thresh_max=100)
#s_binary = s_channel_threshold(img, sthresh=(170,255))
#preprocessImage[((gradx == 1) | (s_binary == 1))] = 1
preprocessImage = np.zeros_like(img[:,:,0])
gradx = abs_sobel_thresh(img, orient='x', thresh_min=12, thresh_max=255)
grady = abs_sobel_thresh(img, orient='y', thresh_min=25, thresh_max=255)
c_binary = color_threshold(img, sthresh=(100,255), vthresh=(50,255))
preprocessImage[((gradx == 1) & (grady ==1) | (c_binary == 1))] = 255
binaryImage = np.copy(preprocessImage)
binaryImage = np.array(cv2.merge((binaryImage,binaryImage,binaryImage)),np.uint8)
cv2.putText(binaryImage, 'Binary Image',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
img_size = (img.shape[1],img.shape[0])
src = np.float32([[img.shape[1]*(0.5-mid_width/2), img.shape[0]*height_pct],[img.shape[1]*(0.5+mid_width/2),img.shape[0]*height_pct],[img.shape[1]*(0.5+bot_width/2), img.shape[0]*bottom_trim],[img.shape[1]*(0.5-bot_width/2), img.shape[0]*bottom_trim]])
offset = img_size[0]*0.25
dst = np.float32([[offset,0],[img_size[0]-offset,0],[img_size[0]-offset,img_size[1]],[offset,img_size[1]]])
#perform the warp perspective transform
M = cv2.getPerspectiveTransform(src,dst) #M
Minv = cv2.getPerspectiveTransform(dst,src)
#############
######################
warped = cv2.warpPerspective(preprocessImage, M, img_size, flags=cv2.INTER_LINEAR)
window_width = 25 #25
window_height = 80 #80
#set up the overall class to do the lane line tracking
curve_centers = tracker(Mywindow_width=window_width, Mywindow_height=window_height, Mymargin = 25, My_ym = 10/720, My_xm = 4/384, Mysmooth_factor=15)
window_centroids = curve_centers.find_window_centroids(warped)
# Points used to draw all the left and right windows
l_points = np.zeros_like(warped)
r_points = np.zeros_like(warped)
# points used to find the right & left lanes
rightx = []
leftx = []
# Go through each level and draw the windows
for level in range(0,len(window_centroids)):
# Window_mask is a function to draw window areas
# Add center value found in frame to the list of lane points per left, right
leftx.append(window_centroids[level][0])
rightx.append(window_centroids[level][1])
l_mask = window_mask(window_width,window_height,warped,window_centroids[level][0],level)
r_mask = window_mask(window_width,window_height,warped,window_centroids[level][1],level)
# Add graphic points from window mask here to total pixels found
l_points[(l_points == 255) | ((l_mask == 1) ) ] = 255
r_points[(r_points == 255) | ((r_mask == 1) ) ] = 255
# Draw the results
template = np.array(r_points+l_points,np.uint8) # add both left and right window pixels together
zero_channel = np.zeros_like(template) # create a zero color channel
template = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8) # make window pixels green
warpage = np.array(cv2.merge((warped,warped,warped)),np.uint8) # making the original road pixels 3 color channels
result = cv2.addWeighted(warpage, 1, template, 0.5, 0.0) # overlay the original road image with window results
windowfit = np.copy(result)
cv2.putText(windowfit, 'Sliding window results',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
warpage1 = np.copy(warpage)
cv2.putText(warpage1, 'Bird\'s-eye View',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
cv2.line(warpage1, (int(dst[0][0]), int(dst[0][1])), (int(dst[1][0]), int(dst[1][1])), [0,0,255], 10, cv2.LINE_AA)
cv2.line(warpage1, (int(dst[1][0]), int(dst[1][1])), (int(dst[2][0]), int(dst[2][1])), [0,0,255], 10, cv2.LINE_AA)
cv2.line(warpage1, (int(dst[2][0]), int(dst[2][1])), (int(dst[3][0]), int(dst[3][1])), [0,0,255], 10, cv2.LINE_AA)
cv2.line(warpage1, (int(dst[3][0]), int(dst[3][1])), (int(dst[0][0]), int(dst[0][1])), [0,0,255], 10, cv2.LINE_AA)
#fit the lane boundaries to the left, right center positions found
yvals = range(0,warped.shape[0])
res_yvals = np.arange(warped.shape[0]-(window_height/2),0,-window_height)
left_fit = np.polyfit(res_yvals, leftx, 2)
left_fitx = left_fit[0]*yvals*yvals + left_fit[1]*yvals + left_fit[2]
left_fitx = np.array(left_fitx,np.int32)
right_fit = np.polyfit(res_yvals, rightx, 2)
right_fitx = right_fit[0]*yvals*yvals + right_fit[1]*yvals + right_fit[2]
right_fitx = np.array(right_fitx,np.int32)
left_lane = np.array(list(zip(np.concatenate((left_fitx-window_width/2, left_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)
right_lane = np.array(list(zip(np.concatenate((right_fitx-window_width/2, right_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)
middle_marker = np.array(list(zip(np.concatenate((right_fitx-window_width/2, right_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)
inner_lane = np.array(list(zip(np.concatenate((left_fitx+window_width/2, right_fitx[::-1]-window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)
road = np.zeros_like(img)
road_bkg = np.zeros_like(img)
cv2.fillPoly(road,[left_lane],color=[255,0,0])
cv2.fillPoly(road,[right_lane],color=[0,0,255])
cv2.fillPoly(road,[inner_lane],color=[0,255,0])
cv2.fillPoly(road_bkg,[left_lane],color=[255,255,255])
cv2.fillPoly(road_bkg,[right_lane],color=[255,255,255])
#Results screen portion for polynomial fit
road1 = np.copy(road)
cv2.putText(road1, 'Polynomial fit',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
road_warped = cv2.warpPerspective(road,Minv,img_size,flags=cv2.INTER_LINEAR)
road_warped_bkg= cv2.warpPerspective(road_bkg,Minv,img_size,flags=cv2.INTER_LINEAR)
base = cv2.addWeighted(img,1.0,road_warped, -1.0, 0.0)
result = cv2.addWeighted(base,1.0,road_warped, 1.0, 0.0)
ym_per_pix = curve_centers.ym_per_pix # meters per pixel in y dimension
xm_per_pix = curve_centers.xm_per_pix # meters per pixel in x dimension
#plt.imshow(result)
#plt.title('Final image results')
#plt.show()
curve_fit_cr = np.polyfit(np.array(res_yvals,np.float32)*ym_per_pix,np.array(leftx,np.float32)*xm_per_pix,2)
curverad = ((1 + (2*curve_fit_cr[0]*yvals[-1]*ym_per_pix + curve_fit_cr[1])**2)**1.5) /np.absolute(2*curve_fit_cr[0])
# Calculate the offset of the car on the road
camera_center = (left_fitx[-1] + right_fitx[-1])/2
center_diff = (camera_center-warped.shape[1]/2)*xm_per_pix
side_pos = 'Right'
if center_diff <= 0:
side_pos = 'left'
#extract left rigth wheel turning
# draw the text showing curvature, offset & speed
cv2.putText(result, 'Radius of Curvature='+str(round(curverad,3))+'m ',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
#cv2.putText(result, 'Vehicle is '+str(abs(round(center_diff,3)))+'m '+side_pos+' of center',(50,100), cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(result, 'Turn Wheel '+str(abs(round(center_diff,3)))+'m '+side_pos,(50,100), cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
#file.write("turnwheel" + center_diff + side_pos +"/n")
height, width = 1080, 1920
FinalScreen = np.zeros((height, width, 3), dtype=np.uint8)
FinalScreen[0:1080,0:1920] = cv2.resize(result, (1920,1080), interpolation=cv2.INTER_AREA)
# FinalScreen[0:360,1280:1920] = cv2.resize(warptrap, (640,360), interpolation=cv2.INTER_AREA)
# FinalScreen[360:720,1280:1920] = cv2.resize(binaryImage, (640,360), interpolation=cv2.INTER_AREA)
# FinalScreen[720:1080,1280:1920] = cv2.resize(warpage1, (640,360), interpolation=cv2.INTER_AREA)
# FinalScreen[720:1080,0:640] = cv2.resize(windowfit, (640,360), interpolation=cv2.INTER_AREA)
# FinalScreen[720:1080,640:1280] = cv2.resize(road1, (640,360), interpolation=cv2.INTER_AREA)
return FinalScreen
#Output_video = 'output1_tracked.mp4'
Input_video = 'h2o.mp4'
Output_video = 'h2finalundistort.mp4'
#Input_video = 'harder_challenge_video.mp4'
#Output_video = 'output_challenge_video.mp4'
#Input_video = 'challenge_video.mp4'
clip1 = VideoFileClip(Input_video)
video_clip = clip1.fl_image(process_image) # This function expects color images
video_clip.write_videofile(Output_video, audio=False)
而不是视频文件作为输入，我想使用屏幕录制并使用 cv2.imshow(result) 显示 def live 的输出
解决方案
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