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from interactive_pipe import interactive_pipeline, interactive, Image
import numpy as np
# Helper functions
# ----------------
def flip_image(img, flip=True, mirror=True):
img = img[::-1] if flip else img
img = img[:, ::-1] if mirror else img
return img
def get_crop(img, pos_x, pos_y, crop_size=0.1):
c_size = int(crop_size * img.shape[0])
crop_x = (int(pos_x * img.shape[1]), int((pos_x) * img.shape[1]) + c_size)
crop_y = (int(pos_y * img.shape[0]), int((pos_y) * img.shape[0]) + c_size)
return crop_x, crop_y
# Processing blocks
# -----------------
@interactive(seed=(45, [0, 100], "Puzzle seed"))
def generate_random_puzzle(seed: int = 45, context: dict = {}):
np.random.seed(seed)
pos_x, pos_y, intensity = np.random.uniform(0.2, 0.8, 3)
context["puzzle"] = (pos_x, pos_y, intensity)
context["puzzle_flip_mirror"] = np.random.choice([True, False], 2)
def create_puzzle(img, context: dict = {}):
out = img.copy()
x_gt, y_gt, intensity = context["puzzle"]
flip_gt, mirror_gt = context["puzzle_flip_mirror"]
cs_x, cs_y = get_crop(img, x_gt, y_gt)
crop = img[cs_y[0]:cs_y[1], cs_x[0]:cs_x[1], ...]
out[cs_y[0]:cs_y[1], cs_x[0]:cs_x[1]] = intensity*0.4*crop
crop = flip_image(crop, flip=flip_gt, mirror=mirror_gt)
return out, crop
@interactive(
flip=(True, "Flip Image"),
mirror=(True, "Mirror Image"),
)
def flip_mirror_piece(piece: np.ndarray, flip=True, mirror=True):
return flip_image(piece.copy(), flip=flip, mirror=mirror)
@interactive(
pos_x=(0.5, [0.1, 0.9], "Position X"),
pos_y=(0.5, [0.1, 0.9], "Position Y"),
)
def place_puzzle(puzzle, piece, pos_x: float = 0.5, pos_y: float = 0.5):
out = puzzle.copy()
cp_x, cp_y = get_crop(img, pos_x, pos_y)
out[cp_y[0]:cp_y[1], cp_x[0]:cp_x[1]] = piece
return out
# pipeline definition
# -------------------
def captcha_pipe(inp):
generate_random_puzzle()
puzzle, puzzle_piece = create_puzzle(inp)
puzzle_piece = flip_mirror_piece(puzzle_piece)
puzzle = place_puzzle(puzzle, puzzle_piece)
return [puzzle_piece, puzzle]
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-b", "--backend", default="gradio",
choices=["gradio", "qt"], type=str)
args = parser.parse_args()
markdown_description = "# Code to build this app on gradio \n"
markdown_description += "```python\n"+open(__file__, 'r').read()+"```"
img = Image.load_image("sample.jpg")
captcha_pipe_interactive = interactive_pipeline(
gui=args.backend,
cache=True,
markdown_description=markdown_description
)(captcha_pipe)
captcha_pipe_interactive(img)
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