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import cv2
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
# input_video = 'car.mp4'
# video Inference
def vid_inf(vid_path):
# Create a VideoCapture object
cap = cv2.VideoCapture(vid_path)
# get the video frames' width and height for proper saving of videos
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_size = (frame_width, frame_height)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
output_video = "output_recorded.mp4"
# create the VideoWriter() object
out = cv2.VideoWriter(output_video, fourcc, fps, frame_size)
# Create Background Subtractor MOG2 object
backSub = cv2.createBackgroundSubtractorMOG2()
# Check if camera opened successfully
if not cap.isOpened():
print("Error opening video file")
count = 0
# Read until video is completed
while cap.isOpened():
# Capture frame-by-frame
ret, frame = cap.read()
# print(frame.shape)
if ret:
# Apply background subtraction
fg_mask = backSub.apply(frame)
# print(fg_mask.shape)
# cv2.imshow('Frame_bg', fg_mask)
# apply global threshol to remove shadows
retval, mask_thresh = cv2.threshold(
fg_mask, 180, 255, cv2.THRESH_BINARY)
# cv2.imshow('frame_thresh', mask_thresh)
# set the kernal
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# Apply erosion
mask_eroded = cv2.morphologyEx(mask_thresh, cv2.MORPH_OPEN, kernel)
# cv2.imshow('frame_erode', mask_eroded)
# Find contours
contours, hierarchy = cv2.findContours(
mask_eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# print(contours)
min_contour_area = 1000 # Define your minimum area threshold
large_contours = [
cnt for cnt in contours if cv2.contourArea(cnt) > min_contour_area]
# frame_ct = cv2.drawContours(frame, large_contours, -1, (0, 255, 0), 2)
frame_out = frame.copy()
for cnt in large_contours:
# print(cnt.shape)
x, y, w, h = cv2.boundingRect(cnt)
frame_out = cv2.rectangle(
frame, (x, y), (x+w, y+h), (0, 0, 200), 3)
frame_out_display = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB)
vid = out.write(frame_out)
# Display the resulting frame
# cv2.imshow('Frame_final', frame_out)
# update the count every frame and display every 12th frame
if not count % 12:
yield frame_out_display, None
count += 1
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
break
else:
break
# When everything done, release the video capture and writer object
cap.release()
out.release()
# Closes all the frames
cv2.destroyAllWindows()
yield None, output_video
# vid_inf(input_video)
# gradio interface
input_video = gr.Video(label="Input Video")
output_frames = gr.Image(label="Output Frames")
output_video_file = gr.Video(label="Output video")
# sample_video=r'sample/car.mp4'
app = gr.Interface(
fn=vid_inf,
inputs=[input_video],
outputs=[output_frames, output_video_file],
title=f"MotionScope",
description=f'A gradio app for dynamic video analysis tool that leverages advanced background subtraction and contour detection techniques to identify and track moving objects in real-time.',
allow_flagging="never",
examples=[["sample/car.mp4"]],
)
app.queue().launch() |