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gaussian.py
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from math import exp
import numpy as np
import cv2
import os
import imgproc
class GaussianTransformer(object):
def __init__(self, imgSize=512, region_threshold=0.4,
affinity_threshold=0.2):
distanceRatio = 3.34
scaledGaussian = lambda x: exp(-(1 / 2) * (x ** 2))
self.region_threshold = region_threshold
self.imgSize = imgSize
self.standardGaussianHeat = self._gen_gaussian_heatmap(imgSize, distanceRatio)
_, binary = cv2.threshold(self.standardGaussianHeat, region_threshold * 255, 255, 0)
np_contours = np.roll(np.array(np.where(binary != 0)), 1, axis=0).transpose().reshape(-1, 2)
x, y, w, h = cv2.boundingRect(np_contours)
self.regionbox = np.array([[x, y], [x + w, y], [x + w, y + h], [x, y + h]], dtype=np.int32)
# print("regionbox", self.regionbox)
_, binary = cv2.threshold(self.standardGaussianHeat, affinity_threshold * 255, 255, 0)
np_contours = np.roll(np.array(np.where(binary != 0)), 1, axis=0).transpose().reshape(-1, 2)
x, y, w, h = cv2.boundingRect(np_contours)
self.affinitybox = np.array([[x, y], [x + w, y], [x + w, y + h], [x, y + h]], dtype=np.int32)
# print("affinitybox", self.affinitybox)
self.oribox = np.array([[0, 0, 1], [imgSize - 1, 0, 1], [imgSize - 1, imgSize - 1, 1], [0, imgSize - 1, 1]],
dtype=np.int32)
def _gen_gaussian_heatmap(self, imgSize, distanceRatio):
scaledGaussian = lambda x: exp(-(1 / 2) * (x ** 2))
heat = 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)
heat[i, j] = np.clip(scaledGaussianProb * 255, 0, 255)
return heat
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 add_region_character(self, image, target_bbox, regionbox=None):
if np.any(target_bbox < 0) or np.any(target_bbox[:, 0] > image.shape[1]) or np.any(
target_bbox[:, 1] > image.shape[0]):
return image
affi = False
if regionbox is None:
regionbox = self.regionbox.copy()
else:
affi = True
M = cv2.getPerspectiveTransform(np.float32(regionbox), np.float32(target_bbox))
oribox = np.array(
[[[0, 0], [self.imgSize - 1, 0], [self.imgSize - 1, self.imgSize - 1], [0, self.imgSize - 1]]],
dtype=np.float32)
test1 = cv2.perspectiveTransform(np.array([regionbox], np.float32), M)[0]
real_target_box = cv2.perspectiveTransform(oribox, M)[0]
# print("test\ntarget_bbox", target_bbox, "\ntest1", test1, "\nreal_target_box", real_target_box)
real_target_box = np.int32(real_target_box)
real_target_box[:, 0] = np.clip(real_target_box[:, 0], 0, image.shape[1])
real_target_box[:, 1] = np.clip(real_target_box[:, 1], 0, image.shape[0])
# warped = cv2.warpPerspective(self.standardGaussianHeat.copy(), M, (image.shape[1], image.shape[0]))
# warped = np.array(warped, np.uint8)
# image = np.where(warped > image, warped, image)
if np.any(target_bbox[0] < real_target_box[0]) or (
target_bbox[3, 0] < real_target_box[3, 0] or target_bbox[3, 1] > real_target_box[3, 1]) or (
target_bbox[1, 0] > real_target_box[1, 0] or target_bbox[1, 1] < real_target_box[1, 1]) or (
target_bbox[2, 0] > real_target_box[2, 0] or target_bbox[2, 1] > real_target_box[2, 1]):
# if False:
warped = cv2.warpPerspective(self.standardGaussianHeat.copy(), M, (image.shape[1], image.shape[0]))
warped = np.array(warped, np.uint8)
image = np.where(warped > image, warped, image)
# _M = cv2.getPerspectiveTransform(np.float32(regionbox), np.float32(_target_box))
# warped = cv2.warpPerspective(self.standardGaussianHeat.copy(), _M, (width, height))
# warped = np.array(warped, np.uint8)
#
# # if affi:
# # print("warped", warped.shape, real_target_box, target_bbox, _target_box)
# # cv2.imshow("1123", warped)
# # cv2.waitKey()
# image[ymin:ymax, xmin:xmax] = np.where(warped > image[ymin:ymax, xmin:xmax], warped,
# image[ymin:ymax, xmin:xmax])
else:
xmin = real_target_box[:, 0].min()
xmax = real_target_box[:, 0].max()
ymin = real_target_box[:, 1].min()
ymax = real_target_box[:, 1].max()
width = xmax - xmin
height = ymax - ymin
_target_box = target_bbox.copy()
_target_box[:, 0] -= xmin
_target_box[:, 1] -= ymin
_M = cv2.getPerspectiveTransform(np.float32(regionbox), np.float32(_target_box))
warped = cv2.warpPerspective(self.standardGaussianHeat.copy(), _M, (width, height))
warped = np.array(warped, np.uint8)
if warped.shape[0] != (ymax - ymin) or warped.shape[1] != (xmax - xmin):
print("region (%d:%d,%d:%d) warped shape (%d,%d)" % (
ymin, ymax, xmin, xmax, warped.shape[1], warped.shape[0]))
return image
# if affi:
# print("warped", warped.shape, real_target_box, target_bbox, _target_box)
# cv2.imshow("1123", warped)
# cv2.waitKey()
image[ymin:ymax, xmin:xmax] = np.where(warped > image[ymin:ymax, xmin:xmax], warped,
image[ymin:ymax, xmin:xmax])
return image
def add_affinity_character(self, image, target_bbox):
return self.add_region_character(image, target_bbox, self.affinitybox)
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_affinity_character(image, affinity.copy()), np.expand_dims(affinity, axis=0)
def generate_region(self, image_size, bboxes):
height, width = image_size[0], image_size[1]
target = np.zeros([height, width], dtype=np.uint8)
for i in range(len(bboxes)):
character_bbox = np.array(bboxes[i].copy())
for j in range(bboxes[i].shape[0]):
target = self.add_region_character(target, character_bbox[j])
return target
def generate_affinity(self, image_size, bboxes, words):
height, width = image_size[0], image_size[1]
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
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.polylines(warped_color, [np.reshape(self.regionbox, (-1, 1, 2))], True, (255, 255, 255), thickness=1)
cv2.imwrite(os.path.join(images_folder, 'standard_color.jpg'), warped_color)
standardGaussianHeat1 = self.standardGaussianHeat.copy()
threshhold = self.region_threshold * 255
standardGaussianHeat1[standardGaussianHeat1 > 0] = 255
threshhold_guassian = cv2.applyColorMap(standardGaussianHeat1, cv2.COLORMAP_JET)
cv2.polylines(threshhold_guassian, [np.reshape(self.regionbox, (-1, 1, 2))], True, (255, 255, 255), thickness=1)
cv2.imwrite(os.path.join(images_folder, 'threshhold_guassian.jpg'), threshhold_guassian)
if __name__ == '__main__':
gaussian = GaussianTransformer(512, 0.4, 0.2)
gaussian.saveGaussianHeat()
gaussian._test()
bbox0 = np.array([[[0, 0], [100, 0], [100, 100], [0, 100]]])
image = np.zeros((500, 500), np.uint8)
# image = gaussian.add_region_character(image, bbox)
bbox1 = np.array([[[100, 0], [200, 0], [200, 100], [100, 100]]])
bbox2 = np.array([[[100, 100], [200, 100], [200, 200], [100, 200]]])
bbox3 = np.array([[[0, 100], [100, 100], [100, 200], [0, 200]]])
bbox4 = np.array([[[96, 0], [151, 9], [139, 64], [83, 58]]])
# image = gaussian.add_region_character(image, bbox)
# print(image.max())
image = gaussian.generate_region((500, 500, 1), [bbox4])
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(image.copy() / 255)
cv2.imshow("test", target_gaussian_heatmap_color)
cv2.imwrite("test.jpg", target_gaussian_heatmap_color)
cv2.waitKey()
# 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)
# # 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=1.70):
# 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)
# #print(warped.shape,(width, height))
#
# return warped, width, height
#
# 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, width, height = self.four_point_transform(bbox.astype(np.float32))
# if width * height < 10:
# return image
#
# 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)
# coding=utf-8
# coding=utf-8