1. Introduction to HSV color space
HSV(Hue, Saturation, Brightness Value) is a color space created based on the intuitive characteristics of color, also be called hexagonal cone model.
【HSV color space model】
2. HSV color space model(gray BGR HSV)
In OpenCV, we often use only two color space conversion methods, namely BGR-> Gray and BGR-> HSV.
!Note: Gray and HSV cannot be converted to each other.
Color space conversion function: cv2.cvtColor (input_image, flag)
BGR-> Gray: flag is cv2.COLOR_BGR2GRAY
BGR-> HSV: flag is cv2.COLOR_BGR2HSV
Value range of HSV color space in OpenCV:
H--[0, 179] S --[0, 255] V--[0, 255]
【Range of commonly used colors】
Code path:
/home/pi/Yahboom_Project/Raspbot/3.AI Vision course/02.Color recognition
import enum
import cv2
def bgr8_to_jpeg(value, quality=75):
return bytes(cv2.imencode('.jpg', value)[1])
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import cv2
import traitlets
import ipywidgets.widgets as widgets
from IPython.display import display
import time
import threading
import inspect
import ctypes
image_widget = widgets.Image(format='jpeg', width=320, height=240)
display(image_widget)
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def _async_raise(tid, exctype):
"""raises the exception, performs cleanup if needed"""
tid = ctypes.c_long(tid)
if not inspect.isclass(exctype):
exctype = type(exctype)
res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))
if res == 0:
raise ValueError("invalid thread id")
elif res != 1:
# """if it returns a number greater than one, you're in trouble,
# and you should call it again with exc=NULL to revert the effect"""
ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)
def stop_thread(thread):
_async_raise(thread.ident, SystemExit)
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image = cv2.VideoCapture(0)
image.set(3, 320)
image.set(4, 240)
image.set(5, 30)
image.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
image.set(cv2.CAP_PROP_BRIGHTNESS, 62)
image.set(cv2.CAP_PROP_CONTRAST, 63)
image.set(cv2.CAP_PROP_EXPOSURE, 4800)
ret, frame = image.read()
image_widget.value = bgr8_to_jpeg(frame)
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import numpy as np
global color_lower
color_lower = np.array([0, 43, 46])
global color_upper
color_upper = np.array([10, 255, 255])
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def Color_Recongnize():
t_start = time.time()
fps = 0
while True:
ret, frame = image.read()
frame = cv2.resize(frame, (320, 240))
frame_ = cv2.GaussianBlur(frame,(5,5),0)
hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv,color_lower,color_upper)
mask = cv2.erode(mask,None,iterations=2)
mask = cv2.dilate(mask,None,iterations=2)
mask = cv2.GaussianBlur(mask,(5,5),0)
cnts = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
if len(cnts) > 0:
cnt = max (cnts, key = cv2.contourArea)
(color_x,color_y),color_radius = cv2.minEnclosingCircle(cnt)
if color_radius > 10:
cv2.circle(frame,(int(color_x),int(color_y)),int(color_radius),(255,0,255),2)
# Proportion-Integration-Differentiation
fps = fps + 1
mfps = fps / (time.time() - t_start)
cv2.putText(frame, "FPS " + str(int(mfps)), (40,40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 3)
image_widget.value = bgr8_to_jpeg(frame)
# print(g_mode)
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thread1 = threading.Thread(target=Color_Recongnize)
thread1.setDaemon(True)
thread1.start()
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stop_thread(thread1)
image.release()
#After using, we need to release the object, otherwise it will be occupied and cannot be used in the next program
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