July 31, 2026 - # Let's see for ourselves. if int(major_ver) < 3 : fps = video.get(cv2.CAP_PROP_FPS) print("Frames per second using video.get(cv2.CAP_PROP_FPS): {0}".format(fps)) else : fps = video.get(cv2.CAP_PROP_FPS) print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps)) # Number of frames to capture num_frames = 120; print("Capturing {0} frames".format(num_frames)) # Start time start = time.perf_counter() # Grab a few frames for i in range(0, num_frames) : ret, frame = video.read() # End time end = time.perf_counter() # Time elapsed seconds = end - start print ("Time taken : {0} seconds".format(seconds)) # Calculate frames per second fps = num_frames / seconds print("Estimated frames per second : {0}".format(fps)) # Release video video.release()

January 4, 2023 - import numpy as np import cv2 import time # creating the videocapture object # and reading from the input file # Change it to 0 if reading from webcam cap = cv2.VideoCapture('vid.mp4') # used to record the time when we processed last frame prev_frame_time = 0 # used to record the time at which we processed current frame new_frame_time = 0 # Reading the video file until finished while(cap.isOpened()): # Capture frame-by-frame ret, frame = cap.read() # if video finished or no Video Input if not ret: break # Our operations on the frame come here gray = frame # resizing the frame size according to

July 17, 2023 - video_capture = cv2.VideoCapture('C:\\Users\\AShetty12\\Downloads\\raw_videos\\MOV_2564.mp4') print(video_capture.isOpened()) # returns True print(video_capture.get(cv2.CAP_PROP_FPS)) # returns 30 total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) print(total_frames) # returns 21784 print(video_capture.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)) # returns True _ = video_capture.read() time_msec = video_capture.get(cv2.CAP_PROP_POS_MSEC) print(time_msec/(1000*total_frames)) # returns 0.03335926980046518 video_capture.get(cv2.CAP_PROP_POS_FRAMES) # returns 21785.0

One of the features we can get is the frames per second (FPS) there are in a video. Frames are the images that make up a video. A video is a series of images shown in sequence. The frames per second tells us how many images there are per second of video. To find the frames per second of a video using OpenCV, we use the following statement, cv2.CAP_PROP_FPS

def clahe(filename): os.chdir(os.path.dirname(filename)) print('Applying CLAHE, this might take awhile...') currentVideo = os.path.basename(filename) fileName, fileEnding = currentVideo.split('.',2) saveName = str('CLAHE_') + str(fileName) + str('.avi') cap = cv2.VideoCapture(currentVideo) imageWidth = int(cap.get(3)) imageHeight = int(cap.get(4)) fps = cap.get(cv2.CAP_PROP_FPS) fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter(saveName, fourcc, fps, (imageWidth, imageHeight), 0) try: while True: ret, image = cap.read() if ret == True: im = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

May 31, 2023 - import cv2 # Create a sample video file path (replace with your video) video_path = 'sample_video.mp4' # Create video capture object cap = cv2.VideoCapture(video_path) # Get video properties fps = cap.get(cv2.CAP_PROP_FPS) total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # Calculate duration in seconds duration_seconds = total_frames / fps print(f"Video Duration: {duration_seconds:.2f} seconds") print(f"Total Frames: {total_frames}") print(f"FPS: {fps}") # Release the video capture object cap.release() Video Duration: 65.00 seconds Total Frames: 1625.0 FPS: 25.0 ·

while True: cap.set(cv2.CAP_PROP_FPS,10) ret,frame = cap.read() print(cap.get(cv2.CAP_PROP_FPS))

April 17, 2021 - Line 15 opens a pointer to the --video file using the cv2.VideoCapture class while Line 16 starts a timer that we can use to measure FPS, or more specifically, the throughput rate of our video processing pipeline.

October 16, 2021 - Hi all, I’ve been playing around with python, opencv, a raspberry 4 and a “pi hq cam”. The thing is, the time it takes to capture is really long. The pi cam could, in theory, do 1920x1080@30fps. I’m not aiming for that but what I’m getting is more like 2-3 fps when using videocapture.

January 26, 2023 - In this lesson we show how to instrument our code to calculate the Frames Per Second (FPS) we are achieving with our camera and OpenCV. We show how to display the FPS as an overlay on our video frame · Accessibility Statement

FPS: {:.2f}".format(fps.fps())) cv2.destroyAllWindows() ... Also the length of original video is 70 seconds. Why I see different results? ... the numbers you see all make sense. the FPS module is unfortunately not part of the opencv codebase, but assuming it "just works" -- if you wait for 25 millis in waitKey() you get 1000 / 25 = 40 FPS, minus some for image drawing, so 38.06 looks quite exact.

April 28, 2025 - cap = cv2.VideoCapture('path_of_input_video_file') ... The get() function is used to retrieve the current frames per second (FPS) of the video.

How do I get the aspect ratio of a pixel using OpenCV? ... Use cv2.waitKey(delayTime) in the loop where you are capturing the frames. This delayTime is in Mili seconds. More the delayTime less will be the fps.