数美的缺口识别 + 轨迹生成
import math
import random
import cv2
import numpy as np
import requests
def get_distance(fg, bg):
fg_image = np.asarray(
bytearray(requests.get(url=fg, verify=False).content), dtype="uint8"
)
fg_image = cv2.imdecode(fg_image, 1)
shape = fg_image.shape
x_points, y_points = [], []
for x_point in range(shape[0]):
for y_point in range(shape[1]):
if list(fg_image[x_point][y_point]) != [0, 0, 0]:
x_points.append(x_point)
y_points.append(y_point)
fg_cut_image = fg_image[
min(x_points) : max(x_points),
]
bg_image = np.asarray(
bytearray(requests.get(url=bg, verify=False).content), dtype="uint8"
)
bg_image = cv2.imdecode(bg_image, 1)
bg_cut_image = bg_image[
min(x_points) : max(x_points),
]
result = cv2.matchTemplate(bg_cut_image, fg_cut_image, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
distance = max_loc[0]
return int(distance)
def get_track(distance: int):
"""
获取轨迹
Args:
distance: 距离
Returns:
"""
x = [0, 0]
y = [0, 0, 0]
z = [0]
count = np.linspace(-math.pi / 2, math.pi / 2, random.randrange(20, 30))
func = list(map(math.sin, count))
nx = [i + 1 for i in func]
add = random.randrange(10, 15)
sadd = distance + add
x.extend(list(map(lambda x: x * (sadd / 2), nx)))
x.extend(np.linspace(sadd, distance, 3 if add > 12 else 2))
x = [math.floor(i) for i in x]
for i in range(len(x) - 2):
if y[-1] < 30:
y.append(y[-1] + random.choice([0, 0, 1, 1, 2, 2, 1, 2, 0, 0, 3, 3]))
else:
y.append(
y[-1] + random.choice([0, 0, -1, -1, -2, -2, -1, -2, 0, 0, -3, -3])
)
for i in range(len(x) - 1):
z.append(
(z[-1] // 100 * 100)
+ 100
+ random.choice([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2])
)
trace = list(map(list, zip(x, y, z)))
times = trace[-1][-1] + random.randint(1, 5)
return trace, times
fg = 'https://castatic.fengkongcloud.cn/crb/slide-atlas-default-without-logo-20230423/v4/c7db06c0414b3faa735cbc324bdfd7ff_fg.png'
bg = 'https://castatic.fengkongcloud.cn/crb/slide-atlas-default-without-logo-20230423/v4/c7db06c0414b3faa735cbc324bdfd7ff_bg.jpg'
print(get_distance(fg, bg))
print(get_track(get_distance(fg, bg)))