pip install Pillow

from PIL import Image

# 打開圖像
img = Image.open("input.jpg")
print(f"格式: {img.format}, 尺寸: {img.size}, 模式: {img.mode}")

# 保存圖像（自動識別格式）
img.save("output.png")

# 轉(zhuǎn)換格式
img.save("converted.webp", "WEBP")

# 調(diào)整尺寸
resized = img.resize((300, 200))

# 旋轉(zhuǎn)（expand參數(shù)防止裁剪）
rotated = img.rotate(45, expand=True)

# 裁剪 (left, upper, right, lower)
cropped = img.crop((100, 100, 400, 400))

# 縮略圖生成（保持比例）
img.thumbnail((128, 128))
img.save("thumbnail.jpg")

from PIL import ImageFilter, ImageEnhance

# 應(yīng)用濾鏡
blurred = img.filter(ImageFilter.GaussianBlur(radius=2))
contour = img.filter(ImageFilter.CONTOUR)

# 顏色調(diào)整
gray = img.convert("L")  # 轉(zhuǎn)為灰度
enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(2.0)  # 對比度增強2倍

# 調(diào)整亮度
bright_enhancer = ImageEnhance.Brightness(img)
bright_img = bright_enhancer.enhance(1.5)

from PIL import ImageDraw, ImageFont

# 創(chuàng)建新畫布
canvas = Image.new("RGB", (500, 500), color=(255, 255, 255))
draw = ImageDraw.Draw(canvas)

# 繪制形狀
draw.rectangle([50, 50, 200, 200], fill="red", outline="blue")
draw.ellipse([250, 250, 400, 400], fill="yellow")

# 添加文字
try:
    font = ImageFont.truetype("arial.ttf", 24)
except IOError:
    font = ImageFont.load_default()
draw.text((100, 300), "Hello Pillow!", fill="black", font=font)

canvas.show()

# 圖像合成
overlay = Image.new("RGBA", img.size, (255, 0, 0, 100))
composite = Image.alpha_composite(img.convert("RGBA"), overlay)

# 像素級操作
pixels = img.load()
for y in range(img.height):
    for x in range(img.width):
        r, g, b = pixels[x, y]
        pixels[x, y] = (r//2, g//2, b//2)  # 暗化效果

# 創(chuàng)建 GIF
images = [img, rotated, cropped]
images[0].save("animation.gif", save_all=True, append_images=images[1:], duration=500, loop=0)

def add_watermark(input_path, output_path, text):
    base = Image.open(input_path).convert("RGBA")
    txt = Image.new("RGBA", base.size, (255,255,255,0))
    
    draw = ImageDraw.Draw(txt)
    font = ImageFont.truetype("arial.ttf", 36)
    
    # 計算文字位置
    text_width, text_height = draw.textsize(text, font)
    x = base.width - text_width - 10
    y = base.height - text_height - 10
    
    draw.text((x, y), text, font=font, fill=(255,255,255,128))
    
    combined = Image.alpha_composite(base, txt)
    combined.convert("RGB").save(output_path)

add_watermark("photo.jpg", "watermarked.jpg", "? Your Brand")

with Image.open("large_image.tiff") as img:
    # 處理圖像

from PIL import Image, ImageChops

# 分離 RGB 通道
r, g, b = img.split()

# 合并時調(diào)整通道順序（例如創(chuàng)建偽紅外效果）
modified = Image.merge("RGB", (b, g, r))

# 通道混合（數(shù)學(xué)運算）
diff = ImageChops.difference(img1, img2)  # 像素差異圖
blended = ImageChops.blend(img1, img2, alpha=0.3)  # 圖像疊加

# CMYK 轉(zhuǎn) RGB
cmyk_img = Image.open("cmyk_image.tiff").convert("RGB")

# 轉(zhuǎn)換為 HSV 顏色空間（需使用第三方庫補充）
# 注意：Pillow 原生不支持 HSV 操作，但可通過 numpy 實現(xiàn)
import numpy as np
hsv_array = np.array(img.convert("HSV"))
# ...處理 HSV 數(shù)組...
modified_img = Image.fromarray(hsv_array, "HSV").convert("RGB")

# 讀取 EXIF 數(shù)據(jù)
exif_data = img._getexif()
if exif_data:
    from PIL.ExifTags import TAGS
    for tag_id, value in exif_data.items():
        tag_name = TAGS.get(tag_id, tag_id)
        print(f"{tag_name}: {value}")

# 旋轉(zhuǎn)圖像（根據(jù) EXIF 方向信息自動修正）
from PIL import ImageOps
fixed_img = ImageOps.exif_transpose(img)

# 分塊處理大圖
tile_size = 512
for y in range(0, img.height, tile_size):
    for x in range(0, img.width, tile_size):
        box = (x, y, x+tile_size, y+tile_size)
        tile = img.crop(box)
        # 處理分塊...
        img.paste(tile, box)

# 使用 LA 模式替代 RGBA（節(jié)省 25% 內(nèi)存）
gray_alpha = img.convert("LA")

# 使用 numpy 加速像素處理
import numpy as np
arr = np.array(img)
arr = arr // 2  # 快速暗化處理
fast_processed = Image.fromarray(arr)

from PIL import Image, ImageDraw, ImageFont, ImageFilter
import random

def generate_captcha(text, size=(200, 80)):
    img = Image.new("RGB", size, (255,255,255))
    draw = ImageDraw.Draw(img)
    
    # 繪制干擾線
    for _ in range(8):
        start = (random.randint(0, size[0]), random.randint(0, size[1]))
        end = (random.randint(0, size[0]), random.randint(0, size[1]))
        draw.line([start, end], fill=(random.randint(0,255), random.randint(0,255), random.randint(0,255)), width=2)
    
    # 添加文字
    font = ImageFont.truetype("arial.ttf", 36)
    text_width, text_height = draw.textbbox((0,0), text, font=font)[2:]
    x = (size[0] - text_width) / 2
    y = (size[1] - text_height) / 2
    
    # 文字扭曲
    for i, char in enumerate(text):
        draw.text(
            (x + i*30 + random.randint(-5,5), y + random.randint(-5,5)),
            char,
            font=font,
            fill=(random.randint(0,160), random.randint(0,160), random.randint(0,160))
        )
    
    # 添加濾鏡
    img = img.filter(ImageFilter.SMOOTH_MORE)
    return img

captcha = generate_captcha("3A4B")
captcha.save("captcha.jpg")

import os
from PIL import Image, ImageOps

def batch_process(input_dir, output_dir, process_func):
    os.makedirs(output_dir, exist_ok=True)
    for filename in os.listdir(input_dir):
        if filename.lower().split('.')[-1] not in ['jpg', 'png', 'jpeg']:
            continue
        
        with Image.open(os.path.join(input_dir, filename)) as img:
            processed = process_func(img)
            processed.save(os.path.join(output_dir, filename))

# 示例處理函數(shù)：轉(zhuǎn)換為素描風(fēng)格
def sketch_effect(img):
    gray = img.convert("L")
    invert = ImageOps.invert(gray)
    blur = invert.filter(ImageFilter.GaussianBlur(3))
    return ImageChops.dodge(gray, blur)

batch_process("./photos", "./processed", sketch_effect)

histogram = img.histogram()
# 獲取 R 通道直方圖（前256個值）
red_hist = histogram[:256]

# 繪制簡易直方圖（需 matplotlib）
import matplotlib.pyplot as plt
plt.figure(figsize=(10,4))
plt.title("Red Channel Histogram")
plt.bar(range(256), red_hist, color='red')
plt.show()

from PIL import ImageFilter

edges = img.convert("L").filter(ImageFilter.FIND_EDGES)
edges = edges.point(lambda x: 255 if x > 50 else 0)  # 二值化
edges.show()

try:
    with Image.open("problematic.jpg") as img:
        # 處理圖像...
except IOError as e:
    print(f"無法打開圖像文件: {str(e)}")
except Image.DecompressionBombError:
    print("圖像尺寸過大，請檢查是否安全！")

# 強制釋放圖像內(nèi)存
img.close()  # 顯式關(guān)閉

# 使用 with 語句自動管理
with Image.open("large_image.tiff") as img:
    thumbnail = img.copy()
    thumbnail.thumbnail((1024, 1024))
thumbnail.save("small.jpg")  # 注意：此時原圖已關(guān)閉

import cv2
import numpy as np
from PIL import Image

# Pillow -> OpenCV
pil_img = Image.open("demo.jpg")
cv_img = np.array(pil_img.convert('RGB'))[:, :, ::-1]  # RGB to BGR

# OpenCV -> Pillow
cv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(cv_img)

import matplotlib.pyplot as plt

plt.imshow(img)
plt.axis('off')
plt.title("Pillow Image Display")
plt.show()

from PIL import ImageFilter

class CustomFilter(ImageFilter.BuiltinFilter):
    name = "CustomSharpen"
    filterargs = (3, 3), 16, 0, (
        -1, -1, -1,
        -1, 24, -1,
        -1, -1, -1
    )

sharpened = img.filter(CustomFilter())

# 安裝額外解碼器后，可支持 WebP 動畫等
# 需安裝：pip install pillow-heif
import pillow_heif
heif_file = pillow_heif.open_heif("image.heif")
heif_img = Image.frombytes(
    heif_file.mode,
    heif_file.size,
    heif_file.data,
    "raw",
    heif_file.mode,
    heif_file.stride,
)

img.save("optimized.jpg", 
        quality=85,        # 質(zhì)量值 (1-100)
        optimize=True,    # 啟用優(yōu)化
        progressive=True) # 漸進(jìn)式JPEG

with Image.open("animation.gif") as img:
    for frame in ImageSequence.Iterator(img):
        # 處理每一幀
        frame.save(f"frame_{frame.tell()}.png")

import numpy as np
from PIL import Image

def histogram_matching(source, template):
    """ 將源圖像的直方圖匹配到模板圖像 """
    src = np.array(source.convert('L')).flatten()
    tgt = np.array(template.convert('L')).flatten()
    
    # 計算累積分布函數(shù)
    src_vals, src_counts = np.unique(src, return_counts=True)
    tgt_vals, tgt_counts = np.unique(tgt, return_counts=True)
    
    src_cdf = np.cumsum(src_counts) / src.size
    tgt_cdf = np.cumsum(tgt_counts) / tgt.size
    
    # 創(chuàng)建映射表
    lut = np.interp(src_cdf, tgt_cdf, tgt_vals)
    return source.point(lut)

source_img = Image.open("landscape.jpg")
template_img = Image.open("sunset.jpg")
matched = histogram_matching(source_img, template_img)

from PIL import ImageOps

# Otsu 自動閾值算法
gray = img.convert('L')
thresh = gray.point(lambda x: 255 if x > 130 else 0)  # 手動閾值

# 自動閾值（需要 scikit-image）
from skimage.filters import threshold_otsu
arr = np.array(gray)
thresh_val = threshold_otsu(arr)
auto_thresh = Image.fromarray((arr > thresh_val).astype(np.uint8) * 255)

# 需要安裝 pydicom
import pydicom
from PIL import Image

ds = pydicom.dcmread("MRI.dcm")
arr = ds.pixel_array.astype(float)
arr = (arr - arr.min()) / (arr.max() - arr.min()) * 255
medical_img = Image.fromarray(arr.astype(np.uint8))
medical_img.save("mri_preview.png")

# 加載多波段圖像
with Image.open("satellite.tif") as img:
    bands = [img.getchannel(band) for band in ('R', 'G', 'B', 'NIR')]

# 計算 NDVI（歸一化植被指數(shù)）
red = np.array(bands[0], dtype=np.float32)
nir = np.array(bands[3], dtype=np.float32)
ndvi = (nir - red) / (nir + red + 1e-10)  # 防止除以零

# 可視化 NDVI
ndvi_img = Image.fromarray(np.uint8((ndvi + 1) * 127.5))
ndvi_img.save("ndvi_map.png")

import glob
from PIL import Image, ImageMath

# 加載聚焦堆棧圖像
z_images = [Image.open(f) for f in sorted(glob.glob("z_stack_*.tif"))]

# 創(chuàng)建全聚焦圖像
def focus_metric(pixels):
    return np.std(pixels)  # 使用標(biāo)準(zhǔn)差作為清晰度指標(biāo)

depth_map = np.zeros(z_images[0].size[::-1])
final_img = np.zeros_like(np.array(z_images[0]))

for i in range(z_images[0].height):
    for j in range(z_images[0].width):
        pixels = [np.array(img)[i,j] for img in z_images]
        sharpness = [focus_metric(p) for p in pixels]
        best_idx = np.argmax(sharpness)
        final_img[i,j] = pixels[best_idx]
        depth_map[i,j] = best_idx

Image.fromarray(final_img).save("focused.tif")
Image.fromarray(np.uint8(depth_map * 255 / len(z_images))).save("depth_map.png")

import cupy as cp
from PIL import Image

def gpu_filter(img):
    # 轉(zhuǎn)換到 GPU
    img_gpu = cp.asarray(img)
    
    # 自定義核函數(shù)（邊緣檢測）
    kernel = cp.array([[-1, -1, -1],
                      [-1,  8, -1],
                      [-1, -1, -1]], dtype=cp.float32)
    
    # 卷積運算
    filtered = cp.zeros_like(img_gpu)
    for c in range(3):
        filtered[:, :, c] = cp.convolve2d(img_gpu[:, :, c], kernel, mode='same')
    
    # 返回 CPU
    return Image.fromarray(cp.asnumpy(filtered).astype(np.uint8))

original = Image.open("gpu_demo.jpg")
accelerated = gpu_filter(original)

from torchvision import transforms
from PIL import Image

class AdvancedAugmentation:
    def __init__(self):
        self.transform = transforms.Compose([
            transforms.RandomApply([
                lambda x: transforms.functional.adjust_sharpness(x, 2),
                lambda x: ImageEnhance.Color(x).enhance(0.8)
            ], p=0.5),
            transforms.RandomPerspective(distortion_scale=0.3),
            transforms.ColorJitter(brightness=0.2, contrast=0.2)
        ])
    
    def __call__(self, img):
        return self.transform(img)

augmenter = AdvancedAugmentation()
img = Image.open("training_sample.jpg")
augmented = augmenter(img)

from PIL import Image, ImageFile

class RAWDecoder(ImageFile.PyDecoder):
    def decode(self, buffer):
        # 實現(xiàn)自定義 RAW 格式解析
        raw_data = self._parse_custom_format(buffer)
        self.set_as_raw(bytes(raw_data))
        return -1, 0

# 注冊解碼器
Image.register_decoder("MYRAW", RAWDecoder)
Image.register_extensions("MYRAW", [".raw"])

from PIL import ImageFilter

class KaleidoscopeFilter(ImageFilter.BuiltinFilter):
    name = "Kaleidoscope"
    filterargs = (5, 5), 16, 0, (
        1, 0, 0, 0, 1,
        0, 0, 0, 0, 0,
        0, 0, 0, 0, 0,
        0, 0, 0, 0, 0,
        1, 0, 0, 0, 1
    )

img.filter(KaleidoscopeFilter()).show()

# 使用 FastAPI 構(gòu)建 REST API
from fastapi import FastAPI, File, UploadFile
from PIL import Image
import io

app = FastAPI()

@app.post("/enhance")
async def enhance_image(file: UploadFile = File(...)):
    img = Image.open(io.BytesIO(await file.read()))
    
    # 執(zhí)行處理管線
    enhanced = (img.convert('RGB')
                .filter(ImageFilter.SHARPEN)
                .enhance(contrast=1.2)
                .resize((1024, 768)))
    
    byte_arr = io.BytesIO()
    enhanced.save(byte_arr, format='JPEG')
    return Response(content=byte_arr.getvalue(), media_type="image/jpeg")

import unittest
from PIL import Image, ImageChops

class ImageTests(unittest.TestCase):
    def test_watermark(self):
        original = Image.new('RGB', (800, 600), 'white')
        watermarked = add_watermark(original.copy(), "Test")
        
        # 驗證水印位置和內(nèi)容
        diff = ImageChops.difference(original, watermarked)
        self.assertGreater(diff.getbbox()[2], 700)  # 檢查右下角變化
        
    def test_performance(self):
        large_img = Image.new('RGB', (10000, 10000))
        start = time.time()
        large_img.thumbnail((1000, 1000))
        self.assertLess(time.time() - start, 1.0)

# 結(jié)合 Pillow 與深度學(xué)習(xí)模型
def style_transfer(content_img, style_img):
    # 加載預(yù)訓(xùn)練模型（需安裝 torch 和 torchvision）
    from torchvision.models import vgg19
    model = vgg19(pretrained=True).features.eval()
    
    # 特征提取與風(fēng)格遷移算法...
    # （此處實現(xiàn)具體遷移邏輯）
    
    return Image.fromarray(output)

content = Image.open("photo.jpg")
style = Image.open("starry_night.jpg")
artwork = style_transfer(content, style)

from PIL import Image, ImageDraw
import qrcode

def animated_qr(data, logo_path):
    base = qrcode.make(data).convert('RGBA')
    logo = Image.open(logo_path).resize((100, 100))
    
    frames = []
    for angle in range(0, 360, 30):
        rotated_logo = logo.rotate(angle)
        frame = base.copy()
        frame.alpha_composite(rotated_logo, 
            ((frame.width-100)//2, (frame.height-100)//2))
        frames.append(frame)
    
    frames[0].save("qr_animation.gif", 
                  save_all=True, 
                  append_images=frames[1:], 
                  duration=200, 
                  loop=0,
                  optimize=True)

import tracemalloc
from PIL import Image

tracemalloc.start()

# 測試代碼塊
with Image.open("large.tiff") as img:
    processed = img.resize((2000, 2000))
    processed.save("output.jpg")

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
print("[ 內(nèi)存消耗 Top 10 ]")
for stat in top_stats[:10]:
    print(stat)

# 使用 cProfile 分析處理流程
python -m cProfile -s cumulative image_processor.py

# 安全字體加載方案
def safe_font_loading(font_path, fallback_size=20):
    try:
        return ImageFont.truetype(font_path, size=fallback_size)
    except IOError:
        print(f"字體 {font_path} 加載失敗，使用系統(tǒng)默認(rèn)字體")
        return ImageFont.load_default()

from pathlib import Path

input_path = Path("input_images") / "special_chars_測試.jpg"
output_path = input_path.with_name(f"processed_{input_path.name}")