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59 | import cv2
from sklearn.metrics import mean_squared_error
from math import sqrt
import numpy as np
import traceback as tb
import images_to_gif as ig
from PIL import Image
cap = cv2.VideoCapture('..\\test\\videoplayback.mp4')
# Check if camera opened successfully
if (cap.isOpened()== False):
print("Error opening video file")
farmes_list = list()
while(cap.isOpened()):
ret, frame = cap.read()
ret, frame = cap.read()
if ret == True:
# Display the resulting frame
cv2.imshow('Frame', frame)
farmes_list.append(frame)
else:
break
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
break
print(f'length of the frame list is= {len(farmes_list)}')
i = 0
new_frame = list()
for img in farmes_list:
try:
frame = img
# Open image in bwDir - The searched image
searchedImageBw = np.array(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
# Open image to be compared
inx = i
if inx != len(farmes_list):
cmpImage = np.array(cv2.cvtColor(farmes_list[inx+1], cv2.COLOR_BGR2GRAY))
rms = sqrt(mean_squared_error(searchedImageBw, cmpImage))
print(f'rms= {rms}')
if rms>3:
#farmes_list.remove(frame)
new_frame.append(frame)
except Exception as e:
print(e)
tb.print_exc()
pass
i = i+1
print(f'length of the frame list is= {len(new_frame)}')
pil_frame = [ Image.fromarray(img) for img in new_frame]
bytesio_object = ig.frame_gif(pil_frame)
ig.save(bytesio_object, path = "videotogif.gif")
cap.release()
cv2.destroyAllWindows()
|
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