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gaussian.py
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gaussian.py
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# coding=utf-8
from math import exp
import numpy as np
import cv2
import os
import imgproc
class GaussianTransformer(object):
def __init__(self, imgSize=512, distanceRatio=1.50):
scaledGaussian = lambda x: exp(-(1 / 2) * (x ** 2))
self.standardGaussianHeat = np.zeros((imgSize, imgSize), np.uint8)
for i in range(imgSize):
for j in range(imgSize):
distanceFromCenter = np.linalg.norm(np.array([i - imgSize / 2, j - imgSize / 2]))
distanceFromCenter = distanceRatio * distanceFromCenter / (imgSize / 2)
scaledGaussianProb = scaledGaussian(distanceFromCenter)
self.standardGaussianHeat[i, j] = np.clip(scaledGaussianProb * 255, 0, 255)
#print("gaussian heatmap min pixel is", self.standardGaussianHeat.min() / 255)
# self.standardGaussianHeat[self.standardGaussianHeat < (0.4 * 255)] = 255
self._test()
def _test(self):
sigma = 10
spread = 3
extent = int(spread * sigma)
center = spread * sigma / 2
gaussian_heatmap = np.zeros([extent, extent], dtype=np.float32)
for i_ in range(extent):
for j_ in range(extent):
gaussian_heatmap[i_, j_] = 1 / 2 / np.pi / (sigma ** 2) * np.exp(
-1 / 2 * ((i_ - center - 0.5) ** 2 + (j_ - center - 0.5) ** 2) / (sigma ** 2))
gaussian_heatmap = (gaussian_heatmap / np.max(gaussian_heatmap) * 255).astype(np.uint8)
images_folder = os.path.abspath(os.path.dirname(__file__)) + '/images'
threshhold_guassian = cv2.applyColorMap(gaussian_heatmap, cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(images_folder, 'test_guassian.jpg'), threshhold_guassian)
def four_point_transform(self, target_bbox, save_dir=None):
'''
:param target_bbox:目标bbox
:param save_dir:如果不是None，则保存图片到save_dir中
:return:
'''
width, height = np.max(target_bbox[:, 0]).astype(np.int32), np.max(target_bbox[:, 1]).astype(np.int32)
right = self.standardGaussianHeat.shape[1] - 1
bottom = self.standardGaussianHeat.shape[0] - 1
ori = np.array([[0, 0], [right, 0],
[right, bottom],
[0, bottom]], dtype="float32")
M = cv2.getPerspectiveTransform(ori, target_bbox)
warped = cv2.warpPerspective(self.standardGaussianHeat.copy(), M, (width, height))
warped = np.array(warped, np.uint8)
if save_dir:
warped_color = cv2.applyColorMap(warped, cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(save_dir, 'warped.jpg'), warped_color)
return warped
def add_character(self, image, bbox):
if np.any(bbox < 0) or np.any(bbox[:, 0] > image.shape[1]) or np.any(bbox[:, 1] > image.shape[0]):
return image
top_left = np.array([np.min(bbox[:, 0]), np.min(bbox[:, 1])]).astype(np.int32)
bbox -= top_left[None, :]
transformed = self.four_point_transform(bbox.astype(np.float32))
try:
score_map = image[top_left[1]:top_left[1] + transformed.shape[0],
top_left[0]:top_left[0] + transformed.shape[1]]
score_map = np.where(transformed > score_map, transformed, score_map)
image[top_left[1]:top_left[1] + transformed.shape[0],
top_left[0]:top_left[0] + transformed.shape[1]] = score_map
except Exception as e:
print(e)
return image
def add_affinity(self, image, bbox_1, bbox_2):
center_1, center_2 = np.mean(bbox_1, axis=0), np.mean(bbox_2, axis=0)
tl = np.mean([bbox_1[0], bbox_1[1], center_1], axis=0)
bl = np.mean([bbox_1[2], bbox_1[3], center_1], axis=0)
tr = np.mean([bbox_2[0], bbox_2[1], center_2], axis=0)
br = np.mean([bbox_2[2], bbox_2[3], center_2], axis=0)
affinity = np.array([tl, tr, br, bl])
return self.add_character(image, affinity.copy()), np.expand_dims(affinity, axis=0)
def generate_region(self, image_size, bboxes):
height, width, channel = image_size
target = np.zeros([height, width], dtype=np.uint8)
for i in range(len(bboxes)):
character_bbox = np.array(bboxes[i])
for j in range(bboxes[i].shape[0]):
target = self.add_character(target, character_bbox[j])
return target
def saveGaussianHeat(self):
images_folder = os.path.abspath(os.path.dirname(__file__)) + '/images'
cv2.imwrite(os.path.join(images_folder, 'standard.jpg'), self.standardGaussianHeat)
warped_color = cv2.applyColorMap(self.standardGaussianHeat, cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(images_folder, 'standard_color.jpg'), warped_color)
standardGaussianHeat1 = self.standardGaussianHeat.copy()
standardGaussianHeat1[standardGaussianHeat1 < (0.4 * 255)] = 255
threshhold_guassian = cv2.applyColorMap(standardGaussianHeat1, cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(images_folder, 'threshhold_guassian.jpg'), threshhold_guassian)
def generate_affinity(self, image_size, bboxes, words):
height, width, channel = image_size
target = np.zeros([height, width], dtype=np.uint8)
affinities = []
for i in range(len(words)):
character_bbox = np.array(bboxes[i])
total_letters = 0
for char_num in range(character_bbox.shape[0] - 1):
target, affinity = self.add_affinity(target, character_bbox[total_letters],
character_bbox[total_letters + 1])
affinities.append(affinity)
total_letters += 1
if len(affinities) > 0:
affinities = np.concatenate(affinities, axis=0)
return target, affinities
if __name__ == '__main__':
gaussian = GaussianTransformer(1024, 1.5)
gaussian.saveGaussianHeat()
bbox = np.array([[[1, 200], [510, 200], [510, 510], [1, 510]]])
print(bbox.shape)
bbox = bbox.transpose((2, 1, 0))
print(bbox.shape)
weight, target = gaussian.generate_target((1024, 1024, 3), bbox.copy())
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(weight.copy() / 255)
cv2.imshow('test', target_gaussian_heatmap_color)
cv2.waitKey()
cv2.imwrite("test.jpg", target_gaussian_heatmap_color)