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| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File : src\services\hcaptcha_challenger\river_challenger.py
# @Time : 2022-03-01 20:32:08
# @Author : Bingjie Yan
# @Email : bj.yan.pa@qq.com
# @License : Apache License 2.0
import cv2
import numpy as np
import skimage
from skimage.morphology import disk
from skimage.segmentation import watershed, slic, mark_boundaries
from skimage.filters import rank
from skimage.util import img_as_ubyte
from skimage.color import rgb2gray, label2rgb
from skimage.future import graph
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
def _weight_mean_color(graph, src, dst, n):
"""Callback to handle merging nodes by recomputing mean color.
The method expects that the mean color of `dst` is already computed.
Parameters
----------
graph : RAG
The graph under consideration.
src, dst : int
The vertices in `graph` to be merged.
n : int
A neighbor of `src` or `dst` or both.
Returns
-------
data : dict
A dictionary with the `"weight"` attribute set as the absolute
difference of the mean color between node `dst` and `n`.
"""
diff = graph.nodes[dst]['mean color'] - graph.nodes[n]['mean color']
diff = np.linalg.norm(diff)
return {'weight': diff}
def merge_mean_color(graph, src, dst):
"""Callback called before merging two nodes of a mean color distance graph.
This method computes the mean color of `dst`.
Parameters
----------
graph : RAG
The graph under consideration.
src, dst : int
The vertices in `graph` to be merged.
"""
graph.nodes[dst]['total color'] += graph.nodes[src]['total color']
graph.nodes[dst]['pixel count'] += graph.nodes[src]['pixel count']
graph.nodes[dst]['mean color'] = (graph.nodes[dst]['total color'] /
graph.nodes[dst]['pixel count'])
class RiverChallenger(object):
def __init__(self) -> None:
pass
def challenge(self, img_stream):
img_arr = np.frombuffer(img_stream, np.uint8)
img = cv2.imdecode(img_arr, flags=1)
height, width = img.shape[:2]
# # filter
img = cv2.pyrMeanShiftFiltering(img, sp=10, sr=40)
img = cv2.bilateralFilter(img, d=9, sigmaColor=100, sigmaSpace=75)
# # enhance brightness
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# img_hsv[:, :, 2] = img_hsv[:, :, 2] * 1.2
# img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
labels = slic(img, compactness=30, n_segments=400, start_label=1)
g = graph.rag_mean_color(img, labels)
labels2 = graph.merge_hierarchical(labels,
g,
thresh=35,
rag_copy=False,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
# view results
out = label2rgb(labels2, img, kind='avg', bg_label=0)
out = mark_boundaries(out, labels2, (0, 0, 0))
skimage.io.imshow(out)
skimage.io.show()
print(np.unique(labels2[-1]))
ref_value = len(np.unique(labels2[-1]))
return ref_value >= 3
if __name__ == '__main__':
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# result_path = 'result.txt'
# if os.path.exists(result_path):
# os.remove(result_path)
# result_file = open(result_path, 'w')
result_file = sys.stdout
base_path = os.path.join('database', '_challenge')
list_dirs = os.listdir(base_path)
for dir in list_dirs[:1]:
print(dir, file=result_file)
for i in range(1, 10):
img_filepath = os.path.join(base_path, dir, f'挑战图片{i}.png')
with open(img_filepath, "rb") as file:
data = file.read()
rc = RiverChallenger()
result = rc.challenge(data)
print(f'挑战图片{i}.png:{result}', file=result_file)
|
