def example2():
data_1 = load_data_from_file("test_data.txt")
data_2 = load_data_from_file("test_data2.txt")
hm = HeatMap(data_1)
hit_img = hm.clickmap()
hm2 = HeatMap(data_2)
hit_img2 = hm2.clickmap(base=hit_img, color=(0, 0, 255, 255))
hit_img2.save("hit2.png")
def main():
# download test data
# url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
# sdata = urllib.request.urlopen(url).read().split("\n")
with open("test_data.txt", 'r') as f:
sdata = [line.replace("\n", "") for line in f.readlines()]
data = []
for ln in sdata:
a = ln.split(",")
if len(a) != 2:
continue
a = [int(i) for i in a]
data.append(a)
# data = get_test_data()
# list -> np.ndarray
data = np.array(data)
bg_image = get_bg()
starttime = datetime.datetime.now()
# start painting
for i in range(5):
hm = HeatMap(data, )
doHeatmap(hm, base=bg_image)
endtime = datetime.datetime.now()
print(f"It costs {(endtime - starttime).seconds}s.")
def test():
print("load data..")
mins = []
data = []
url = 'http://apluspre.taobao.ali.com/aplus/pub/udataResult.htm?spmId=1.6659421&action=udataAction&event_submit_do_getHotData=y'
sdata = urllib.urlopen(url).read().split("\n")
for ln in sdata:
a = ln.split(",")
if len(a) != 3 or abs(int(a[0])) > 700 or int(a[1]) > 10000:
continue
mins.append(int(a[0]))
imin = min(mins)
if imin >= 0:
imin = 0
for ln in sdata:
a = ln.split(",")
if len(a) != 3:
continue
a = [int(a[0]) - imin, int(a[1]), int(a[2])]
data.append(a)
starttime = time.clock()
print("painting..")
hm = HeatMap(data, None, 1400, 10000)
hm.heatmap(save_as="heat.png")
endtime = time.clock()
print(endtime - starttime)
print("done.")
def heatmap(img, output, img_num, num_ppl):
print('generating heat map for img', img_num)
out_cpu = output.cpu().detach()
den = out_cpu[0].permute(1, 2, 0).numpy()
den = den[:, :, 0]
print('output shape:{}'.format(den.shape))
den_resized = np.zeros(
(den.shape[0] * SCALING_FACTOR, den.shape[1] * SCALING_FACTOR))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / SCALING_FACTOR)][int(
j / SCALING_FACTOR)] / (SCALING_FACTOR * SCALING_FACTOR)
den = den_resized
den = den * 10 / np.max(den)
img_text = write_text(img, num_ppl)
img_path = "out/" + str(img_num) + ".jpg"
cv2.imwrite(img_path, img_text)
w = img.shape[1]
h = img.shape[0]
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
hm = HeatMap(data, base=img_path)
hm.heatmap(save_as='out/' + 'heat_' + '_' + str(img_num) + '_' +
str(int(num_ppl)) + '.jpg')
def getHeatMap(request):
url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
sdata = urllib.urlopen(url).read().split("\n")
mins = []
data = []
for ln in sdata:
a = ln.split(",")
if len(a) != 3:
continue
mins.append(int(a[0]))
imin = min(mins)
for ln in sdata:
a = ln.split(",")
if len(a) != 3:
continue
a = [int(a[0]) + imin, int(a[1]), int(a[2])]
data.append(a)
hm = HeatMap(data, None, 1400, 10000)
hm.heatmap(save_as="heat.png")
return HttpResponse("complete")
def heatmap(den, img):
den_resized = np.zeros((den.shape[0] * 4, den.shape[1] * 4))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / 4)][int(j / 4)] / 16
den = den_resized
count = np.sum(den)
data2 = np.asarray(den * 25)
histogram = np.sum(data2, axis=0)
histogram[histogram < 0] = 0
histogram = np.around(histogram)
x_range = [np.min(histogram), np.max(histogram)]
plt.xlabel("Position along the frame width")
plt.ylabel("Number of People")
plt.plot(list(range(data2.shape[1])), histogram)
plt.savefig('./test/count_plot.jpg')
den = den * 10 / np.max(den)
w = img.shape[1]
h = img.shape[0]
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
hm = HeatMap(data, base='./test/img.jpg')
return hm
def heatmap(den, img_num, dataset, info):
print('generating heat map for img', img_num)
print('shape:', den.shape)
if info == 'pre':
den_resized = np.zeros((den.shape[0] * 4, den.shape[1] * 4))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / 4)][int(j / 4)] / 16
den = den_resized
img_path = './data/original/shanghaitech/part_' + dataset + '_final/test_data/images/'
count = np.sum(den)
den = den * 10 / np.max(den)
img = cv2.imread(img_path + 'IMG_' + str(img_num) + '.jpg', 1)
w = img.shape[1]
h = img.shape[0]
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
hm = HeatMap(data, base=img_path + 'IMG_' + str(img_num) + '.jpg')
def density_heatmap(width, height, box_centers, r=4):
'''
制作热力图
'''
hm = HeatMap(box_centers, width=width, height=height)
heatmap_img = hm.heatmap(r=r)
# 将PIL转opencv
heatmap_img = cv2.cvtColor(np.asarray(heatmap_img), cv2.COLOR_RGB2BGR)
return heatmap_img
def example1():
# 加载测试数据
data = loadDataFromFile("test_data.txt")
# 开始绘制
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
def save_heatmap(density_map,
img,
img_name,
output_dir,
down_sample=True,
gt=False):
"""
生成热力图并保存
:param density_map: 2d-array, 密度图
:param img: numpy [B,H,W,1]
:param img_name: "abc.jpg"
:param output_dir: 结果保存目录
:param down_sample: bool, 是否有下采样
:param gt: bool, 是否生成gt的热力图
:return:
"""
counts = int(np.sum(density_map)) # 人数
print('generating heatmap for', img_name)
# 如果密度图进行下采样4倍, 则需要还原到原始大小
if down_sample:
h, w = density_map.shape
den_resized = np.zeros((h * 4, w * 4))
for i in range(h * 4):
for j in range(w * 4):
den_resized[i][j] = density_map[int(i / 4)][int(j / 4)] / 16
density_map = den_resized
density_map = density_map * 1000
data = []
h, w = img.shape[1:3]
for row in range(h):
for col in range(w):
try:
for k in range(int(density_map[row][col])):
data.append([col + 1, row + 1])
except IndexError:
continue
# 生成heatmap
hm = HeatMap(data, width=w, height=h)
# 保存heatmap
hm_name = 'heatmap_' + img_name.split('.')[0] + '.png'
hm.heatmap(save_as=os.path.join(output_dir, hm_name))
# 使用蓝色填充heatmap背景, 并显示人群数量
im = Image.open(os.path.join(output_dir, hm_name))
x, y = im.size
bg = Image.new('RGBA', im.size, (0, 0, 139))
bg.paste(im, (0, 0, x, y), im)
im_arr = np.array(bg)
text = 'GT Count: {}'.format(counts) if gt else 'Est Count: {}'.format(
counts)
cv2.putText(im_arr, text, (10, y - 20), cv2.FONT_HERSHEY_PLAIN, 1,
(0, 0, 0), 1)
im = Image.fromarray(im_arr)
im.save(os.path.join(output_dir, hm_name))
def example1():
# 加载测试数据
data = load_data_from_file("test_data.txt")
# 开始绘制
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
# hm.heatmap(save_as="heat.png", r=20) # 这儿可以传入 r 参数，指定热图半径，默认 r=10
# hm.heatmap(save_as="heat.png", base="/var/tmp/test_base.png")
hm.heatmap(save_as="heat.png")
def generate_heatmap(array, percentile=0.5, sign=''):
per = np.percentile(array, 1 - percentile)
height, width = array.shape
hdata = []
for i in range(height):
for j in range(width):
if (array[i][j] > per):
hdata.append([i, j])
hm = HeatMap(hdata)
hm.clickmap(save_as='hit' + str(sign) + '.png')
hm.heatmap(save_as='heat' + str(sign) + '.png')
def main(opt):
minus = np.array([[0, 1, 0], [0, -1, 0], [1, 0, 0], [-1, 0, 0]])
# Gets all data points, and transformed into (x,y)
pos = []
with open(opt.input, 'r') as file:
for line in file.readlines():
r, theta = map(lambda x: float(x), line[:-1].split(','))
pos.append([
int(opt.scale * r * math.cos(toPhi(theta))),
int(-opt.scale * r * math.sin(toPhi(theta)))
])
pos = np.asarray(pos)
minimum = -np.min(pos)
pos = pos - np.min(pos)
# pos = np.apply_along_axis(lambda l: np.append(l, opt.duplicate), 1, pos)
ans = np.copy(pos)
if SHIFT:
for i in range(minus.shape[0]):
ans = np.concatenate(
(ans, np.apply_along_axis(lambda l: l - minus[i], 1, pos)))
print(ans.shape)
hm = HeatMap(ans.tolist())
# ------------ Draw all points ------------
hm.clickmap(save_as=opt.input + "_hit.png")
img = hm.heatmap()
draw = ImageDraw.Draw(img)
unit = 5
font = ImageFont.truetype("/Library/Fonts/Arial.ttf", size=40)
for i in range(5):
radius = unit * i * opt.scale
draw.ellipse((minimum - radius, minimum - radius, minimum + radius,
minimum + radius),
outline='black')
draw.text(toxy((minimum, minimum), radius, 130),
"%d\u00B0" % (unit * i),
'black',
font=font)
halfline = 5 * unit * opt.scale
draw.line([(minimum - halfline, minimum), (minimum + halfline, minimum)],
fill='black')
draw.line([(minimum, minimum - halfline), (minimum, minimum + halfline)],
fill='black')
img.save(opt.input + "_heat.png")
print("Two files generated!")
def heatmap(den, num, dataset, info):
###################################################
#img_num = num + 600
print('generating heat map for img', img_num)
print('shape:', den.shape)
if info == 'pre':
den_resized = np.zeros((den.shape[0] * 8, den.shape[1] * 8))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / 8)][int(j / 8)] / 64
den = den_resized
#img_path = './data/original/shanghaitech/part_' + dataset + '_final/test_data/images/'
img_path = './data/ucsd/test/'
count = np.sum(den)
den = den * 10 / np.max(den)
img = cv2.imread(img_path + 'mall_IMG_' + str(img_num) + '.jpg', 1)
w = img.shape[1]
h = img.shape[0]
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
hm = HeatMap(data, base=img_path + 'IMG_' + str(img_num) + '.jpg')
hm.heatmap(save_as='heat_' + dataset + '_' + str(img_num) + '_' + info +
'_' + str(int(count)) + '.png')
hm2 = HeatMap(data)
hm2.heatmap(save_as='heatalone_' + dataset + '_' + str(img_num) + '_' +
info + '_' + str(int(count)) + '.png')
def main():
url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
print "begin"
sdata = urllib.urlopen(url).read().split("\n")
data = []
for ln in sdata:
a = ln.split(",")
if len(a) != 2:
continue
a = [int(i) for i in a]
data.append(a)
hm = HeatMap(data)
hm.heatmap(save_as="heat.png")
def main():
data = []
# download test data
url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
for line in urllib.request.urlopen(url):
line_str = str(line, encoding='utf-8').split("\n")
for ln in line_str:
if ',' in ln:
a = ln.split(",")
a = [int(i) for i in a]
data.append(a)
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
def main():
# url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
ff = open('./test_data.txt', 'r')
sdata = ff.read().split("\n")
data = []
for ln in sdata:
a = ln.split(",")
if len(a) != 2:
continue
a = [int(i) for i in a]
data.append(a)
# 开始绘制
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
def main(srcUrl, clkMap, heatMap, width, height):
# url = "https://raw.github.com/oldj/pyheatmap/master/examples/test_data.txt"
# sdata = urllib.urlopen(url).read().split("\n")
# url = "D:/DEV/GitHub/Sokaris_View3D/Sokaris_View3D/IntersectionsHN.ini"
sdata = open(srcUrl, 'r').read().split('\n')
data = []
for ln in sdata:
a = ln.split(" ")
if len(a) != 2:
continue
a = [int(i) for i in a]
data.append(a)
hm = HeatMap(data=data, width=width, height=height)
hm.clickmap(save_as=clkMap)
hm.heatmap(save_as=heatMap)
def heatmap(den, base_img_path, n, save_path):
print('generating heatmap for ' + base_img_path)
den_resized = np.zeros((den.shape[0] * n, den.shape[1] * n))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / n)][int(j / n)] / (n**2)
den = den_resized
den = den * 10 / np.max(den)
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
hm = HeatMap(data, base=base_img_path)
hm.heatmap(save_as=save_path)
print('done generating heatmap')
def apply_heatmap(image, data):
'''image是原图，data是坐标'''
'''创建一个新的与原图大小一致的图像，color为0背景为黑色。这里这样做是因为在绘制热力图的时候如果不选择背景图，画出来的图与原图大小不一致（根据点的坐标来的），导致无法对热力图和原图进行加权叠加，因此，这里我新建了一张背景图。'''
background = Image.new("RGB", (image.shape[1], image.shape[0]), color=0)
# 开始绘制热度图
hm = HeatMap(data)
hit_img = hm.heatmap(base=background, r=100) # background为背景图片，r是半径，默认为10
# ~ plt.figure()
# ~ plt.imshow(hit_img)
# ~ plt.show()
#hit_img.save('out_' + image_name + '.jpeg')
hit_img = cv2.cvtColor(np.asarray(hit_img),
cv2.COLOR_RGB2BGR) #Image格式转换成cv2格式
overlay = image.copy()
alpha = 0.5 # 设置覆盖图片的透明度
cv2.rectangle(overlay, (0, 0), (image.shape[1], image.shape[0]),
(255, 0, 0), -1) # 设置蓝色为热度图基本色蓝色
image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0) # 将背景热度图覆盖到原图
image = cv2.addWeighted(hit_img, alpha, image, 1 - alpha, 0) # 将热度图覆盖到原图
def test():
u"""测试方法"""
print("load data..")
data = []
f = open("../examples/test_data.txt")
for ln in f:
a = ln.split(",")
if len(a) != 2:
continue
x, y = int(a[0]), int(a[1])
data.append([x, y])
f.close()
print("painting..")
# 开始绘制
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
print("done.")
import numpy as np
import datetime
import function_2 as func_2
import main_1
import matplotlib.pyplot as plt
from pyheatmap.heatmap import HeatMap
# Get accident location and plot scatter diagram and heat map
acc_origin_path = r'C:/Users/lkr/Desktop/graduate/previous_data/accident_2015.csv' # path for address descriptions
acc_new_path = r'C:/Users/lkr/Desktop/graduate/previous_data/accident_longitude_latitude.csv' # path for longitudes and latitudes
func_2.get_location(acc_origin_path, acc_new_path)
acc_location = pd.read_csv(
r'C:/Users/lkr/Desktop/graduate/previous_data/accident_longitude_latitude.csv'
)
plt.scatter(acc_location, s=20, c='b') # plot scatter diagram
hm = HeatMap(acc_location) # plot heat map
hm.clickmap(save_as='hit.png')
hm.heatmap(save_as='heat.png')
# Accidents-Sample points match
sp_location = pd.read_csv(
r'C:/Users/lkr/Desktop/graduate/previous_data/sample_points_location.csv')
sp_match = []
for i in acc_location:
dist = 99999
sp = [-1, -1]
for j in sp_location:
if dist > func_2.mhd_distance(i, j) > 0.001:
dist = func_2.mhd_distance(i, j)
sp = j
sp_match = [sp_match, sp]
print(np.max(den))
den = den * 500
#print(den)
pic = cv2.imread('IMG_' + str(img) + '.jpg', 0)
w = pic.shape[1]
h = pic.shape[0]
data = []
for j in range(len(den)):
for i in range(len(den[0])):
for k in range(int(den[j][i])):
data.append([i + 1, j + 1])
#print(data)
hm = HeatMap(data, base='IMG_' + str(img) + '.jpg')
hm.heatmap(save_as='heat_' + str(img) + '_with_base.png')
hm = HeatMap(data, width=w, height=h)
hm.heatmap(save_as='heat_' + str(img) + '.png')
def heatmap(den, img_num, dataset, info):
print('generating heat map for img', img_num)
print('shape:', den.shape)
if info == 'pre':
den_resized = np.zeros((den.shape[0] * 4, den.shape[1] * 4))
for i in range(den_resized.shape[0]):
for j in range(den_resized.shape[1]):
den_resized[i][j] = den[int(i / 4)][int(j / 4)] / 16
def heat_map(combine_data):
map_data = excel_table_index(file)
media_names = map_data[0]
resolution = map_data[1]
gaze_points_x = map_data[2]
gaze_points_y = map_data[3]
# GazeEventDuration = map_data[4]
check_name = media_names[0]
image_size = [[0, 0], [resolution[0][0], resolution[0][1]]]
index = 0
point = []
pointslist = []
for name in media_names:
if check_name is not name:
print(check_name)
pointslist.extend(image_size)
image_size.remove(image_size[1])
image_size.append([resolution[index][0], resolution[index][1]])
if combine_data:
for cindex in range(len(combine_data[0])):
if combine_data[0][cindex] == check_name:
pointslist.append([
int(combine_data[1][cindex]),
int(combine_data[2][cindex])
])
hm = HeatMap(pointslist)
hm_names = check_name.split(".")
hm_name = hm_names[0]
# hm.clickmap(save_as=outputpath + hm_name + "_hit.png")
hm.heatmap(save_as=output_path + hm_name + "_heat.png")
pointslist = []
check_name = name
point.append(gaze_points_x[index])
point.append(gaze_points_y[index])
pointslist.append(point)
point = []
if index == len(media_names) - 1:
print(check_name)
pointslist.extend(image_size)
if combine_data:
for cindex in range(len(combine_data[0])):
if combine_data[0][cindex] == check_name:
pointslist.append([
int(combine_data[1][cindex]),
int(combine_data[2][cindex])
])
hm = HeatMap(pointslist)
hm_names = name.split(".")
hm_name = hm_names[0]
# hm.clickmap(save_as=outputpath + hm_name + "_hit.png")
hm.heatmap(save_as=output_path + hm_name + "_heat.png")
break
index += 1
from pyheatmap.heatmap import HeatMap
from PIL import Image
import csv
import math
data = []
minx = 0
miny = 0
basewidth = 500
with open('test.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in reader:
sp = row[0].split(",")
r = float(sp[0])
t = float(sp[1])
x = int(r * math.cos(t))
y = int(r * math.sin(t))
minx = min(x, minx)
miny = min(y, miny)
print(minx, miny)
data.append([x - minx, y - miny])
hm = HeatMap(data)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
im = Image.open("heat.png")
im = im.rotate(270)
print(im.format, im.size, im.mode)
im.save("heat_resize.png", "PNG")
from pyheatmap.heatmap import HeatMap
import matplotlib.pyplot as plt
# file=np.load('COCO_train2014_000000001580.npy','r')
file = np.load('COCO_train2014_000000014537.npy', 'r')
# file=np.load('COCO_val2014_000000000294.npy','r')
f = []
for i in range(640):
s = []
for j in range(502):
s.append(file[i][j][6])
f.append(s)
hm = HeatMap(f)
hm.clickmap(save_as="hit.png")
hm.heatmap(save_as="heat.png")
def apply_heatmap(image, data):
'''image是原图，data是坐标'''
'''创建一个新的与原图大小一致的图像，color为0背景为黑色。这里这样做是因为在绘制热力图的时候如果不选择背景图，画出来的图与原图大小不一致（根据点的坐标来的），导致无法对热力图和原图进行加权叠加，因此，这里我新建了一张背景图。'''
background = Image.new("RGB", (image.shape[1], image.shape[0]), color=0)
# 开始绘制热度图
hm = HeatMap(data)
hit_img = hm.heatmap(base=background, r=100) # background为背景图片，r是半径，默认为10
# ~ plt.figure()
# ~ plt.imshow(hit_img)
# ~ plt.show()
#hit_img.save('out_' + image_name + '.jpeg')