Contour Manipulation

After detection, contours can be manipulated and processed using a variety of techniques, including:

Filtering: Applying filters to smooth or enhance contours.
Morphological Operations: Using operations like dilation, erosion, and closing to modify contour shapes and sizes.
Feature Extraction: Extracting features like centroid, area, perimeter, and orientation from contours for further analysis and classification.

Contour Detection Using OpenCV

OpenCV provides powerful tools and functions to simplify the process of contour detection in images. With functions like findContours(), programmers can avoid writing lengthy algorithms and focus on the core logic of their applications.

Thresholding-Based Preprocessing

In thresholding based process, it tends to separate objects from the background based on their intensity variations (brightness or color). In this part, the color image (img) is converted to grayscale (gray). Then, adaptive thresholding is applied using cv2.adaptiveThreshold().

#conversion
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(
    gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 21, 5)

Edge-Based Preprocessing

Here, edge detection is implicitly performed as part of the contour detection process using the cv2.findContours() function with the cv2.RETR_TREE mode. Detected contours are drawn on a copy of the original image (detected_contours) using cv2.drawContours().

# Finding contours with RETR_TREE mode
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
detected_contours = img.copy()
cv2.drawContours(detected_contours, contours, -1, (0, 255, 0), -1)

Contour Detection and Masking

In this part, contour detection is explicitly performed using the cv2.findContours() function with cv2.RETR_EXTERNAL mode. Two masks are created:

highlight: A colored mask highlighting the contour area.
foreground: A mask used to extract the contours from the original image.

# Finding contours with RETR_EXTERNAL mode for masking
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Masking with color
highlight = np.ones_like(img)
cv2.drawContours(highlight, contours, -1, (0, 200, 175), cv2.FILLED)

# Masking for extraction
mask = np.zeros_like(img)
cv2.drawContours(mask, contours, -1, (255, 255, 255), cv2.FILLED)
foreground = cv2.bitwise_and(img, mask)

Complete Code:

Python3

#imports
import cv2
from matplotlib import pyplot as plt
import numpy as np

#loading img
file = "/content/drive/MyDrive/spiderman.jpg"
img = cv2.imread(file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.show()

#conversion
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(
    gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 21, 5)

#Func to find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
detected_contours = img.copy()
cv2.drawContours(detected_contours, contours, -1, (0, 255, 0), -1)
plt.imshow(detected_contours)
plt.title('Detected contours')
plt.show()

contours, _ = cv2.findContours(
    thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#masking img
highlight = np.ones_like(img)
cv2.drawContours(highlight, contours, -1, (0, 200, 175), cv2.FILLED)
plt.imshow(highlight)
plt.title('Highlight contour with color')
plt.show()

#masking again
mask = np.zeros_like(img)
cv2.drawContours(mask, contours, -1, (255, 255, 255), cv2.FILLED)
foreground = cv2.bitwise_and(img, mask)

#edges
plt.imshow(foreground)
plt.title('Extract contours')
plt.show()

print("\t")

#--main-plot--
contours = {"Original": img, "Detected contours": detected_contours,
            "Color contours": highlight, "Extract contours": foreground}
plt.subplots_adjust(wspace=.2, hspace=.2)
plt.tight_layout()

for i, (key, value) in enumerate(contours.items()):
    plt.subplot(2, 2, i + 1)
    plt.tick_params(labelbottom=False)
    plt.tick_params(labelleft=False)
    plt.title("{}".format(key))
    plt.imshow(value)

plt.show()

Output:

What Are Contours in Computer Vision?Contour Detection Using OpenCV

In computer vision, a contour is like a digital representation of that outline. It can be described as the series of connected points that define the boundary of an object, separating and/or highlighting it from the background. These points tend to share similar color or intensity values, making them distinct from their surroundings. In this article, we are going to explore more on contours in computer vision.

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