OpenCVの特徴的な機能

import cv2
import urllib.request

# 画像のダウンロード
url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

# 色空間変換
img = cv2.imread("fruits.jpg")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cv2.imshow('HSV', hsv)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

# 画像のダウンロード
url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

# 色空間変換
img = cv2.imread("fruits.jpg")
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
cv2.imshow('L*a*b*', lab)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

img = cv2.imread("fruits.jpg", 0)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
cl1 = clahe.apply(img)
cv2.imshow('CLAHE', cl1)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

# 画像のダウンロード
url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

# 色空間変換
img = cv2.imread("fruits.jpg")
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)

# L*a*b*の各チャネルを分離
l, a, b = cv2.split(lab)

# L*成分にCLAHEを適用
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
cl1 = clahe.apply(l)

# チャネルを結合
lab_clahe = cv2.merge([cl1, a, b])

# BGRに戻す
bgr = cv2.cvtColor(lab_clahe, cv2.COLOR_Lab2BGR)
cv2.imshow('Result', bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request

url1 = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/rubberwhale1.png"
url2 = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/rubberwhale2.png"
urllib.request.urlretrieve(url1, "frame1.png")
urllib.request.urlretrieve(url2, "frame2.png")

prev_img = cv2.imread('frame1.png', cv2.IMREAD_GRAYSCALE)
next_img = cv2.imread('frame2.png', cv2.IMREAD_GRAYSCALE)

# オプティカルフローの計算
flow = cv2.calcOpticalFlowFarneback(prev_img, next_img, None, 0.5, 3, 15, 3, 5, 1.2, 0)

# フロー可視化用のHSV画像を生成
hsv = np.zeros((prev_img.shape[0], prev_img.shape[1], 3), dtype=np.uint8)
hsv[..., 1] = 255

# フローの大きさと角度を計算
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)

# HSVからBGRに変換して表示
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

# 結果を表示
cv2.imshow('Optical Flow', bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request
import sys
import os

def print_usage():
    print("Usage:")
    print("  No argument: Download and use sample video (vtest.avi)")
    print("  Number (0,1,...): Use webcam with specified device number")
    print("  Filename: Use specified video file")

# 入力ソースの決定
if len(sys.argv) == 1:
    # 引数なし: サンプル動画をダウンロードして使用
    print("Using sample video (vtest.avi)")
    video_url = "https://github.com/opencv/opencv/raw/master/samples/data/vtest.avi"
    urllib.request.urlretrieve(video_url, "vtest.avi")
    cap = cv2.VideoCapture("vtest.avi")
elif sys.argv[1].isdigit():
    # 数字: WebCamを使用
    device_num = int(sys.argv[1])
    print(f"Using webcam device {device_num}")
    cap = cv2.VideoCapture(device_num)
else:
    # その他: ファイル名として解釈
    filename = sys.argv[1]
    if not os.path.exists(filename):
        print(f"Error: File '{filename}' not found")
        print_usage()
        exit()
    print(f"Using video file: {filename}")
    cap = cv2.VideoCapture(filename)

# 最初のフレームの読み込みとグレースケール変換
ret, prev_frame = cap.read()
if not ret:
    print("Error opening video source")
    print_usage()
    exit()
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)

# HSV画像の準備
hsv = np.zeros_like(prev_frame)
hsv[..., 1] = 255

while True:
    # 次のフレームの読み込み
    ret, next_frame = cap.read()
    if not ret:
        break
    next_gray = cv2.cvtColor(next_frame, cv2.COLOR_BGR2GRAY)

    # オプティカルフローの計算
    flow = cv2.calcOpticalFlowFarneback(prev_gray, next_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)

    # フローの大きさと角度を計算
    mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
    hsv[..., 0] = ang * 180 / np.pi / 2
    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)

    # HSVからBGRに変換して表示
    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    cv2.imshow('Optical Flow', bgr)

    # 'q'キーを押すと終了
    if cv2.waitKey(30) & 0xFF == ord('q'):
        break

    # 現在のフレームを次のループのprev_frameとして設定
    prev_gray = next_gray

cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

img = cv2.imread("fruits.jpg")
rows, cols, ch = img.shape

# 変換元の点を画像サイズ内に制限
pts1 = np.float32([[min(50, cols-1), min(50, rows-1)],
                  [min(200, cols-1), min(50, rows-1)],
                  [min(50, cols-1), min(200, rows-1)]])

# 変換先の点を画像サイズ内に制限
pts2 = np.float32([[min(10, cols-1), min(100, rows-1)],
                  [min(200, cols-1), min(50, rows-1)],
                  [min(100, cols-1), min(250, rows-1)]])

M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow('Affine Transform', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

img = cv2.imread("fruits.jpg", 0)

# kernelサイズを画像サイズに応じて設定
kernel_size = max(3, min(5, img.shape[0]//100))
kernel = np.ones((kernel_size, kernel_size), np.uint8)

erosion = cv2.erode(img, kernel, iterations=1)
cv2.imshow('Erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

img = cv2.imread("fruits.jpg")
lower = cv2.pyrDown(img)
higher = cv2.pyrUp(lower)
cv2.imshow('Lower Resolution', lower)
cv2.imshow('Higher Resolution', higher)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/smarties.png"
urllib.request.urlretrieve(url, "smarties.png")
img = cv2.imread("smarties.png", cv2.IMREAD_COLOR)  # カラー画像として読み込み

# テンプレート領域
template_x, template_y = 350, 50
template_w, template_h = 60, 60
template = img[template_y:template_y+template_h, template_x:template_x+template_w].copy()  # copyで切り出し

# 表示用画像のコピー作成
img_display = img.copy()

# テンプレートマッチング
result = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)

# スコア0.8以上の位置を緑色で表示
locations = np.where(result >= 0.8)
for y, x in zip(*locations):
    _ = cv2.rectangle(img_display, (x, y), (x+template_w, y+template_h), (0,255,0), 2)

# テンプレート位置を赤色で表示
_ = cv2.rectangle(img_display, (template_x,template_y), (template_x+template_w,template_y+template_h), (0,0,255), 2)

cv2.imshow('Template Matching', img_display)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/sudoku.png"
urllib.request.urlretrieve(url, "sudoku.png")
img = cv2.imread("sudoku.png", 0)

# 画像サイズに応じたブロックサイズの設定
blockSize = max(3, min(img.shape[0], img.shape[1]) // 50)
if blockSize % 2 == 0:
   blockSize += 1  # ブロックサイズは奇数である必要がある

thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                            cv2.THRESH_BINARY, blockSize, 2)

cv2.imshow('Adaptive Threshold', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import urllib.request

url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/messi5.jpg"
urllib.request.urlretrieve(url, "messi5.jpg")
img = cv2.imread("messi5.jpg")
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (50, 50, 450, 290)
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img = img * mask2[:, :, np.newaxis]
cv2.imshow('GrabCut', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import urllib.request

# 画像のダウンロード
url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/fruits.jpg"
urllib.request.urlretrieve(url, "fruits.jpg")

# 画像表示
img = cv2.imread("fruits.jpg")
height, width = img.shape[:2]

# 幅を1024以下にするための1/2または1/4倍リサイズ
if width > 1024:
   scale_factor = 0.5 if width / 2 > 1024 else 0.25
   new_width = int(width * scale_factor)
   new_height = int(height * scale_factor)
   resized_img = cv2.resize(img, (new_width, new_height),
                           interpolation=cv2.INTER_AREA)
else:
   resized_img = img

cv2.imshow('Resized Image', resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
# カメラのキャプチャ
cap = cv2.VideoCapture(0)
while cap.isOpened():
   ret, frame = cap.read()
   if not ret:
       break
   height, width = frame.shape[:2]

   # 幅を1024以下にするための1/2または1/4倍リサイズ
   if width > 1024:
       scale_factor = 0.5 if width / 2 > 1024 else 0.25
       new_width = int(width * scale_factor)
       new_height = int(height * scale_factor)
       resized_frame = cv2.resize(frame, (new_width, new_height),
                                interpolation=cv2.INTER_AREA)
   else:
       resized_frame = frame
   cv2.imshow('Webcam', resized_frame)
   if cv2.waitKey(1) & 0xFF == ord('q'):
       break
cap.release()
cv2.destroyAllWindows()