ImageEdit

About

A sandbox for exploring image manipulation with Python. Describe what you want in plain English and an LLM writes a Pillow/OpenCV script — or write your own. Everything runs in-browser via Pyodide, so your images never leave your device. Great for learning Pillow, experimenting with filters, or just messing around.

Example Scripts

arr = np.array(img, dtype=np.float64)
sepia_matrix = np.array([[0.393, 0.769, 0.189],
                         [0.349, 0.686, 0.168],
                         [0.272, 0.534, 0.131]])
out = arr[..., :3] @ sepia_matrix.T
out = np.clip(out, 0, 255).astype(np.uint8)
img = Image.fromarray(out)

arr = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)
edges = cv2.Canny(arr, 80, 150)
img = Image.fromarray(edges)

blurred = img.filter(ImageFilter.GaussianBlur(radius=8))
mask = Image.new('L', img.size, 0)
draw = ImageDraw.Draw(mask)
w, h = img.size
band_top = int(h * 0.35)
band_bot = int(h * 0.65)
draw.rectangle([0, band_top, w, band_bot], fill=255)
mask = mask.filter(ImageFilter.GaussianBlur(radius=30))
img = Image.composite(img, blurred, mask)

arr = np.array(img)
gray = cv2.cvtColor(arr, cv2.COLOR_RGB2GRAY)
cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
faces = cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
for (x, y, w, h) in faces:
    roi = arr[y:y+h, x:x+w]
    arr[y:y+h, x:x+w] = cv2.GaussianBlur(roi, (99, 99), 30)
img = Image.fromarray(arr)

block = 12
w, h = img.size
small = img.resize((w // block, h // block), Image.NEAREST)
img = small.resize((w, h), Image.NEAREST)

arr = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2HSV)
gray = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)
gray_rgb = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
# Red hue wraps around 0/180
mask1 = cv2.inRange(arr, (0, 60, 40), (10, 255, 255))
mask2 = cv2.inRange(arr, (165, 60, 40), (180, 255, 255))
mask = mask1 | mask2
mask = cv2.GaussianBlur(mask, (5, 5), 0)
result = np.where(mask[:,:,None] > 0, np.array(img), gray_rgb)
img = Image.fromarray(result.astype(np.uint8))