Of course! Here is a comprehensive set of OpenCV Python samples, starting from the absolute basics and progressing to more common and practical tasks.
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Prerequisites
First, make sure you have OpenCV and NumPy installed. If not, open your terminal or command prompt and run:
pip install opencv-python pip install numpy pip install matplotlib # Often used for displaying images in notebooks
Sample 1: Reading, Displaying, and Saving an Image
This is the "Hello, World!" of image processing. We'll read an image from a file, show it in a window, and save a modified version.
import cv2
import os
# --- 1. Reading an Image ---
# Make sure you have an image file named 'lena.jpg' in the same directory,
# or provide the full path to an image.
# If you don't have one, you can download one from:
# https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png
image_path = 'lena.jpg'
# Check if the image file exists
if not os.path.exists(image_path):
print(f"Error: Image file not found at '{image_path}'")
# Create a dummy black image for demonstration if the file is missing
print("Creating a dummy black image for demonstration...")
img = np.zeros((512, 512, 3), np.uint8)
cv2.putText(img, "Image Not Found", (100, 250), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
else:
# cv2.IMREAD_COLOR loads the image in BGR format (default)
# cv2.IMREAD_GRAYSCALE loads the image in grayscale
# cv2.IMREAD_UNCHANGED loads the image as-is, including alpha channel
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
# --- 2. Displaying the Image ---
# Create a window to display the image
cv2.imshow('Original Image', img)
# Wait for a key press. The '0' means it will wait indefinitely.
# If you provide a number (e.g., 5000), it will wait for 5000 ms.
print("Press any key to continue...")
cv2.waitKey(0)
# --- 3. Modifying the Image ---
# Let's convert the image to grayscale
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# --- 4. Saving the Image ---
# Save the grayscale image to a new file
output_path = 'lena_gray.jpg'
cv2.imwrite(output_path, gray_image)
print(f"Grayscale image saved to '{output_path}'")
# --- 5. Displaying the Modified Image ---
cv2.imshow('Grayscale Image', gray_image)
print("Press any key to exit...")
cv2.waitKey(0)
# --- 6. Cleaning Up ---
# Close all OpenCV windows
cv2.destroyAllWindows()
Sample 2: Working with Video (From Camera and File)
OpenCV can easily capture and process video streams.
A. Capturing Video from a Webcam
import cv2
# Create a VideoCapture object. 0 is usually the default webcam.
cap = cv2.VideoCapture(0)
# Check if the webcam is opened correctly
if not cap.isOpened():
print("Error: Could not open video device")
exit()
# Loop to read frames from the webcam
while True:
# cap.read() returns a boolean (success) and the video frame
ret, frame = cap.read()
if not ret:
print("Failed to grab frame")
break
# Display the resulting frame
cv2.imshow('Webcam Feed', frame)
# The 'q' key is used to quit the loop
# cv2.waitKey(1) waits for 1ms, allowing the window to update
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture and close windows
cap.release()
cv2.destroyAllWindows()
B. Playing a Video File
This is very similar to the webcam example. Just change the source.
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import cv2
# Create a VideoCapture object for a video file
# Replace 'my_video.mp4' with the path to your video file
cap = cv2.VideoCapture('my_video.mp4')
if not cap.isOpened():
print("Error: Could not open video file")
exit()
while True:
ret, frame = cap.read()
# If the frame is not read correctly, it means the video has ended
if not ret:
print("Video playback finished.")
break
# You can process the frame here, e.g., convert to grayscale
# gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# cv2.imshow('Video Playback (Grayscale)', gray_frame)
cv2.imshow('Video Playback', frame)
# Exit if 'q' is pressed
if cv2.waitKey(25) & 0xFF == ord('q'): # 25ms delay is good for most videos
break
cap.release()
cv2.destroyAllWindows()
Sample 3: Basic Image Operations
These are fundamental operations you'll use constantly.
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load an image
img = cv2.imread('lena.jpg')
if img is None:
print("Image not found. Please run Sample 1 first or provide a valid path.")
exit()
# --- 1. Accessing Pixel Values ---
# Get the pixel value at (100, 100)
pixel_value = img[100, 100]
print(f"Pixel value at (100, 100) in BGR: {pixel_value}")
# Get the Blue, Green, and Red values separately
blue = img[100, 100, 0]
green = img[100, 100, 1]
red = img[100, 100, 2]
print(f"BGR values at (100, 100): B={blue}, G={green}, R={red}")
# --- 2. Modifying Pixel Values ---
# Set a region of interest (ROI) to a specific color
# This will draw a small red square in the top-left corner
img[0:100, 0:100] = [0, 0, 255] # BGR format for Red
# --- 3. Resizing an Image ---
# Resize to 50% of the original size
resized_img = cv2.resize(img, (0,0), fx=0.5, fy=0.5)
# --- 4. Cropping an Image ---
# Crop using array slicing [y_start:y_end, x_start:x_end]
cropped_img = img[100:400, 200:500]
# --- 5. Drawing Shapes and Text ---
drawing_img = img.copy()
# Draw a green diagonal line
cv2.line(drawing_img, (0, 0), (511, 511), (0, 255, 0), 5)
# Draw a blue rectangle
cv2.rectangle(drawing_img, (100, 100), (400, 400), (255, 0, 0), 3)
# Draw a red circle
cv2.circle(drawing_img, (255, 255), 100, (0, 0, 255), -1) # -1 for filled circle
# Add white text
cv2.putText(drawing_img, 'OpenCV', (10, 500), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3)
# --- Displaying all results ---
# Note: cv2.imshow() expects BGR images, matplotlib expects RGB.
# We need to convert for matplotlib.
plt.figure(figsize=(12, 8))
# Original Image
plt.subplot(2, 3, 1)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))"Original")
# Modified Pixel
plt.subplot(2, 3, 2)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))"Modified Pixel (Red Square)")
# Resized
plt.subplot(2, 3, 3)
plt.imshow(cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB))"Resized")
# Cropped
plt.subplot(2, 3, 4)
plt.imshow(cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB))"Cropped")
# Drawing
plt.subplot(2, 3, 5)
plt.imshow(cv2.cvtColor(drawing_img, cv2.COLOR_BGR2RGB))"Drawing Shapes & Text")
plt.show()
Sample 4: Image Processing with Filters
This sample demonstrates common filters like blurring, edge detection, and thresholding.
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load a grayscale image for edge detection and thresholding
img = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)
if img is None:
print("Image not found. Please provide a valid path.")
exit()
# --- 1. Blurring (Smoothing) ---
# Averaging blur
blur_avg = cv2.blur
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