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enhance_detect_red_blue_beacon_animated_plots.py
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enhance_detect_red_blue_beacon_animated_plots.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Nov 27 23:06:25 2023
@author: cosmi
"""
import cv2
import numpy as np
import matplotlib.pyplot as plt
import time
import signal
import os
import warnings
warnings.filterwarnings("ignore")
cap = cv2.VideoCapture('video_test.mp4')
blue_vals = []
red_vals = [] # List to store red values
times = []
start_time = time.time()
def plot_and_save(blue_vals, red_vals, times):
# Plot blue values
plt.figure()
plt.plot(times, blue_vals, color='blue', label='Blue Values')
plt.xlabel('Time (seconds)')
plt.ylabel('Blue Values')
plt.title('Blue Values vs Time')
plt.legend()
# Generate a unique filename for blue values plot based on current timestamp
blue_filename = f"blue_plot_{time.strftime('%Y%m%d-%H%M%S')}.png"
plt.savefig(blue_filename)
plt.close() # Close the figure after saving
plt.show() # Show the blue values plot
# Plot red values
plt.figure()
plt.plot(times, red_vals, color='red', label='Red Values')
plt.xlabel('Time (seconds)')
plt.ylabel('Red Values')
plt.title('Red Values vs Time')
plt.legend()
# Generate a unique filename for red values plot based on current timestamp
red_filename = f"red_plot_{time.strftime('%Y%m%d-%H%M%S')}.png"
plt.savefig(red_filename)
plt.close() # Close the figure after saving
plt.show() # Show the red values plot
def handle_interrupt(signal, frame):
plot_and_save(blue_vals, red_vals, times)
exit(0)
signal.signal(signal.SIGINT, handle_interrupt)
try:
while True:
ret, frame = cap.read()
if ret:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define the blue color range
lower_blue = np.array([100, 60, 60])
upper_blue = np.array([150, 255, 255])
# Define the red color range
lower_red = np.array([0, 70, 50])
upper_red = np.array([10, 255, 255])
lower_red2 = np.array([170, 70, 50])
upper_red2 = np.array([180, 255, 255])
# Mask for blue and red
blue_mask = cv2.inRange(hsv, lower_blue, upper_blue)
red_mask1 = cv2.inRange(hsv, lower_red, upper_red)
red_mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
red_mask = cv2.bitwise_or(red_mask1, red_mask2)
res_blue = cv2.bitwise_and(frame, frame, mask=blue_mask)
res_red = cv2.bitwise_and(frame, frame, mask=red_mask)
# Process blue mask
src_height, src_width, _ = frame.shape
max_value = src_height * src_width * 255
blueVal = float(blue_mask.sum()) / float(max_value)
blue_vals.append(blueVal)
# Process red mask
redVal = float(red_mask.sum()) / float(max_value)
red_vals.append(redVal)
# Process contours for blue
blue_contours, _ = cv2.findContours(cv2.cvtColor(res_blue, cv2.COLOR_BGR2GRAY), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if blue_contours:
for contour in blue_contours:
cv2.drawContours(res_blue, contour, -1, (0, 255, 0), 2)
# Process contours for red
red_contours, _ = cv2.findContours(cv2.cvtColor(res_red, cv2.COLOR_BGR2GRAY), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if red_contours:
for contour in red_contours:
cv2.drawContours(res_red, contour, -1, (0, 0, 255), 2)
# Show the processed results
cv2.imshow('Blue Detection', res_blue)
cv2.imshow('Red Detection', res_red)
elapsed_time = time.time() - start_time
times.append(elapsed_time)
else:
print("Read Failed")
break
if cv2.waitKey(1) & 0xFF == ord("q"):
break
except KeyboardInterrupt:
handle_interrupt(None, None)
# Finally, plot and display before exiting.
plot_and_save(blue_vals, red_vals, times)
cap.release()
cv2.destroyAllWindows()
cap.release()
cv2.destroyAllWindows()