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color_segmetation_video.py
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color_segmetation_video.py
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import cv2
import numpy as np
import time
#import os
def mask_it(img, lw, up):
mask = cv2.inRange(img, lw, up)
result = cv2.bitwise_and(img, img, mask=mask)
return result
def region_of_interest(img):
height = img.shape[0]
width = img.shape[1]
mask = np.ones_like(img)*255
poly = np.array([[ # Ploygon to build custom mask
(0, 0),
(width, 0),
(width, 210),
(0, 210), ]], np.int32)
masked = cv2.fillPoly(mask, poly, 0) # return none --> fills region
# Ou exclusivo para ignorar oq estiver fora da mask
masked_image = cv2.bitwise_and(img, mask)
return masked_image
i = 0
mean_time = 0
cap = cv2.VideoCapture("pista2.MP4")
while(cap.isOpened()):
start = time.time()
i = i+1
_, frame = cap.read()
dst = cv2.GaussianBlur(frame, (7, 7), 0)
length, width, ch = frame.shape
crop = frame[250:length, 0:200, :]
# l1=(100,100,100)
# up1=(101,120,120)
l1 = (95, 95, 95)
up1 = (101, 120, 120) # Still in RGB space!
l2 = (0, 0, 60)
up2 = (180, 40, 120) # Still in RGB space!
trim = region_of_interest(frame)
# hsv = cv2.cvtColor(trim, cv2.COLOR_BGR2HSV)
# cv2.imshow('hsv',hsv)
mask = mask_it(trim, l1, up1)
# mask =mask_it(hsv,l2,up2)
cv2.imshow('mask_it', mask)
kernel = np.ones((7, 7), np.uint8)
# opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernel, iterations = 3)
close = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=7)
op = cv2.morphologyEx(close, cv2.MORPH_OPEN, kernel, iterations=8)
# Se eu continuar, fecha a região certinho =)
combo = cv2.addWeighted(frame, 0.8, op, 1, 1)
################ Calculate the Moments and Visualize ################
# convert image to grayscale image
gray_mask = cv2.cvtColor(op, cv2.COLOR_BGR2GRAY)
# convert the grayscale image to binary image
ret, thresh = cv2.threshold(gray_mask, 0, 255, 0)
# calculate moments of binary image
M = cv2.moments(thresh)
# calculate x,y coordinate of center
try:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
except Exception:
cX = 0
cY = 0
# put text and highlight the center
int(frame.shape[1]/2), frame.shape[0]
cX, cY
# m = (y-y0)/(x-x0) = tan(theta)
rad = np.arctan((cY-frame.shape[0])/(cX-int(frame.shape[1]/2)+0.001))
theta = np.degrees(rad)
# print('Direção: %.5s graus' % theta)
circle = np.zeros_like(frame)
cv2.circle(circle, (cX, cY), 5, (255, 255, 255), -1)
cv2.putText(circle, "centroid", (cX - 25, cY - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.putText(circle, "Direction: %.5s graus" % theta, (25, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# write the lines
cv2.line(circle, (cX, cY),
(int(frame.shape[1]/2), frame.shape[0]), (255, 0, 0), 2)
# display the image
combo2 = cv2.addWeighted(combo, 0.9, circle, 1, 0)
cv2.imshow('mask', op)
cv2.imshow('resultado', combo2)
# Calculate the time taken to process the frame
end = time.time()
mean_time = (mean_time*(i-1) + (end - start))/i
mean_fps = 1/mean_time
if cv2.waitKey(1) & 0xFF == ord('q'):
print("O tempo de execução médio foi de {:.2f} ms, ou {:.2f} FPS.".format(
mean_time*1000, mean_fps))
break
cap.release()
cv2.destroyAllWindows()