HSV color space

Open In Colab

In [1]:
# to run in google colab
In [2]:
# to run in google colab
import sys

import cv2
import matplotlib.pyplot as plt
import numpy as np

if "google.colab" in sys.modules:

    def download_from_web(url):
        import requests

        response = requests.get(url)
        if response.status_code == 200:
            with open(url.split("/")[-1], "wb") as file:
                file.write(response.content)
        else:
            raise Exception(
                f"Failed to download the image. Status code: {response.status_code}"
            )

    download_from_web(
        "https://github.com/YoniChechik/AI_is_Math/raw/master/c_02a_basic_image_processing/grass.jpg"
    )
    download_from_web(
        "https://github.com/YoniChechik/AI_is_Math/raw/master/c_02a_basic_image_processing/hsv_th.png"
    )
In [3]:
# Adopted from: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html

figsize = (10, 10)

Read basic image:

In [4]:
bgr_im = cv2.imread("grass.jpg")
rgb_im = cv2.cvtColor(bgr_im, cv2.COLOR_BGR2RGB)
plt.figure(figsize=figsize)
plt.imshow(rgb_im)
plt.title("original image")
plt.show()
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We want to separate the grass from the sky. we'll do this by masking all pixels in the image that are not green.

First, find HSV green

In [5]:
rgb_green = np.uint8(
    [[[0, 255, 0]]]
)  # 3d array just because this is what cvtColor expects...
hsv_green = cv2.cvtColor(rgb_green, cv2.COLOR_RGB2HSV)[0, 0, :]
print(hsv_green)

[ 60 255 255]

Second, convert the image to HSV and threshold only the green color and neighborhood.

We will take hue TH of +30 and -70 (because it's farther from blue- the sky). Let's take all saturation and value variants of green in the TH.

Masking all that is in the TH should give us only the grass

In [6]:
thresholding_schematics = cv2.cvtColor(cv2.imread("hsv_th.png"), cv2.COLOR_BGR2RGB)
plt.figure(figsize=figsize)
plt.imshow(thresholding_schematics)
plt.title("Thresholding schematics")
plt.show()
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In [7]:
# Convert BGR to HSV
hsv_im = cv2.cvtColor(rgb_im, cv2.COLOR_RGB2HSV)

# define range of hue and intensity
lower_th = hsv_green - np.array([70, 200, 200])
upper_th = hsv_green + np.array([30, 0, 0])

# Threshold the HSV image
mask = cv2.inRange(hsv_im, lower_th, upper_th)

plt.figure(figsize=figsize)
plt.imshow(mask)
plt.title("resulted mask")
plt.show()
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In [8]:
# Trick: apply 2d mask on 3d image
rgb_res = cv2.bitwise_and(rgb_im, rgb_im, mask=mask)

plt.figure(figsize=figsize)
plt.imshow(rgb_res)
plt.title("output image")
plt.show()
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