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使用 Python-OpenCV 实时进行多种颜色检测

原文:https://www.geesforgeks.org/multi-color-detection-in-time-use-python-opencv/

对于一个能够可视化环境的机器人来说，随着物体的检测，实时检测其颜色也是非常重要的。

为什么这很重要？:一些真实世界的应用

  • 在自动驾驶汽车中，检测交通信号。
  • 多颜色检测用于一些工业机器人，在分离不同颜色的物体时执行拾取和放置任务。

这是一个使用 Python 编程语言实时检测多种颜色(这里只考虑了和 蓝色 颜色)的实现。 使用的 Python 库:****

工作流程描述:

*Step 1: Input: Capture video through webcam. Step 2: Read the video stream in image frames. Step 3: Convert the imageFrame in BGR(RGB color space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) color space. Hue describes a color in terms of saturation, represents the amount of gray color in that color and value describes the brightness or intensity of the color. This can be represented as three matrices in the range of 0-179, 0-255 and 0-255 respectively. Step 4: Define the range of each color and create the corresponding mask. Step 5: Morphological Transform: Dilation, to remove noises from the images. Step 6: bitwise_and between the image frame and mask is performed to specificaly detect that particular color and discrad others. Step 7: Create contour for the individual colors to display the detected colored region distinguishly. Step 8: Output: Detection of the colors in real-time.*

下面是实现。

**# Python code for Multiple Color Detection

import numpy as np
import cv2

# Capturing video through webcam
webcam = cv2.VideoCapture(0)

# Start a while loop
while(1):

    # Reading the video from the
    # webcam in image frames
    _, imageFrame = webcam.read()

    # Convert the imageFrame in 
    # BGR(RGB color space) to 
    # HSV(hue-saturation-value)
    # color space
    hsvFrame = cv2.cvtColor(imageFrame, cv2.COLOR_BGR2HSV)

    # Set range for red color and 
    # define mask
    red_lower = np.array([136, 87, 111], np.uint8)
    red_upper = np.array([180, 255, 255], np.uint8)
    red_mask = cv2.inRange(hsvFrame, red_lower, red_upper)

    # Set range for green color and 
    # define mask
    green_lower = np.array([25, 52, 72], np.uint8)
    green_upper = np.array([102, 255, 255], np.uint8)
    green_mask = cv2.inRange(hsvFrame, green_lower, green_upper)

    # Set range for blue color and
    # define mask
    blue_lower = np.array([94, 80, 2], np.uint8)
    blue_upper = np.array([120, 255, 255], np.uint8)
    blue_mask = cv2.inRange(hsvFrame, blue_lower, blue_upper)

    # Morphological Transform, Dilation
    # for each color and bitwise_and operator
    # between imageFrame and mask determines
    # to detect only that particular color
    kernal = np.ones((5, 5), "uint8")

    # For red color
    red_mask = cv2.dilate(red_mask, kernal)
    res_red = cv2.bitwise_and(imageFrame, imageFrame, 
                              mask = red_mask)

    # For green color
    green_mask = cv2.dilate(green_mask, kernal)
    res_green = cv2.bitwise_and(imageFrame, imageFrame,
                                mask = green_mask)

    # For blue color
    blue_mask = cv2.dilate(blue_mask, kernal)
    res_blue = cv2.bitwise_and(imageFrame, imageFrame,
                               mask = blue_mask)

    # Creating contour to track red color
    contours, hierarchy = cv2.findContours(red_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)

    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y), 
                                       (x + w, y + h), 
                                       (0, 0, 255), 2)

            cv2.putText(imageFrame, "Red Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX, 1.0,
                        (0, 0, 255))    

    # Creating contour to track green color
    contours, hierarchy = cv2.findContours(green_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)

    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y), 
                                       (x + w, y + h),
                                       (0, 255, 0), 2)

            cv2.putText(imageFrame, "Green Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX, 
                        1.0, (0, 255, 0))

    # Creating contour to track blue color
    contours, hierarchy = cv2.findContours(blue_mask,
                                           cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            imageFrame = cv2.rectangle(imageFrame, (x, y),
                                       (x + w, y + h),
                                       (255, 0, 0), 2)

            cv2.putText(imageFrame, "Blue Colour", (x, y),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1.0, (255, 0, 0))

    # Program Termination
    cv2.imshow("Multiple Color Detection in Real-TIme", imageFrame)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break**

*输出:*

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