OpenCV（十一）--停车场车位识别
def show(img):
img[0] = cv2.resize(img[0], (900, 600))
img[1] = cv2.resize(img[1], (900, 600))
pic = np.hstack((img[0], img[1]))
cv2.imshow('pic', pic)
cv2.waitKey(0)
cv2.destroyAllWindows()
test_images = [plt.imread(path) for path in glob.glob('test_images/*.png')]
show(test_images)
过滤背景
def select_rgb_white_yellow(img):
# 过滤掉背景
lower = np.float32([0.45, 0.45, 0.45])
upper = np.float32([1.0, 1.0, 1.0])
# 低于lower和高于upper都变成0，其余变成255
white_mask = img.copy()
masked = img.copy()
for i in range(len(img)):
white_mask[i] = cv2.inRange(img[i], lower, upper)
masked[i] = cv2.bitwise_and(img[i], img[i], mask=white_mask[i])
show(white_mask)
show(masked)
return masked
images = select_rgb_white_yellow(test_images)
white_mask
masked
转灰度图
def convert_gray_scale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray_images = list(map(convert_gray_scale, images))
show(gray_images)
边缘检测
def int8(img):
img = img * 255
return img.astype(np.uint8)
def detect_edges(img, low_th=80, high_th=200):
img = int8(img)
return cv2.Canny(img, low_th, high_th)
edge_images = list(map(detect_edges, gray_images))
show(edge_images)
定位区域
def select_region(img):
rows, cols = img.shape[:2]
pt_1 = [cols * 0.05, rows * 0.90]
pt_2 = [cols * 0.05, rows * 0.70]
pt_3 = [cols * 0.30, rows * 0.52]
pt_4 = [cols * 0.60, rows * 0.15]
pt_5 = [cols * 0.90, rows * 0.15]
pt_6 = [cols * 0.90, rows * 0.90]
vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]],dtype=np.int32)
point_img = img.copy()
point_img = cv2.cvtColor(point_img, cv2.COLOR_GRAY2RGB)
for point in vertices[0]:
cv2.circle(point_img, (point[0], point[1]), 10, (0, 0, 255), 4)
return point_img, vertices
roi_images, vertices = list(map(select_region, edge_images))
show(roi_images)
剔除多余区域
def filter_region(img):
rows, cols = img.shape[:2]
pt_1 = [cols * 0.05, rows * 0.90]
pt_2 = [cols * 0.05, rows * 0.70]
pt_3 = [cols * 0.30, rows * 0.52]
pt_4 = [cols * 0.60, rows * 0.15]
pt_5 = [cols * 0.90, rows * 0.15]
pt_6 = [cols * 0.90, rows * 0.90]
vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32)
mask = np.zeros_like(img)
if len(mask.shape) == 2:
cv2.fillPoly(mask, vertices, 255) # 填充
return cv2.bitwise_and(img, mask)
region_images = list(map(filter_region, edge_images))
show(region_images)
检测直线
def hough_lines(img):
# 输入图像是边缘检测后的图像
return cv2.HoughLinesP(img, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4)
list_lines = list(map(hough_lines, region_images))
print(len(list_lines[0]))
画线
def draw_lines(img, lines, color=[255, 0, 0], thickness=2, make_copy=True):
if make_copy:
img = np.copy(img)
cleaned =[]
for line in lines:
for x1, y1, x2, y2 in line:
if abs(y2 - y1) <=1 and abs(x2 - x1) >= 25 and abs(x2 - x1) <= 55 :
cleaned.append((x1, y1, x2, y2))
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
return img
line_images = []
for img, lines in zip(test_images, list_lines):
line_images.append(draw_lines(img, lines))
show(line_images)
定位列
def identify_blocks(img, lines, make_copy=True):
if make_copy:
img = np.copy(img)
# Step1:过滤直线
cleaned =[]
for line in lines:
for x1, y1, x2, y2 in line:
if abs(y2 - y1) <=1 and abs(x2 - x1) >= 25 and abs(x2 - x1) <= 55 :
cleaned.append((x1, y1, x2, y2))
# Step2:过滤后的直线按x1进行排序
import operator
list1 = sorted(cleaned, key=operator.itemgetter(0, 1))
# Step3:找到多个列
clusters = {}
dIndex = 0
clus_dist = 15
for i in range(len(list1) - 1):
distance = abs(list1[i+1][0] - list1[i][0])
if distance <= clus_dist:
if not dIndex in clusters.keys(): clusters[dIndex] = []
clusters[dIndex].append(list1[i])
clusters[dIndex].append(list1[i + 1]) # 会重复加入字典
else:
dIndex += 1
# Step4:得到坐标
rects = {}
i = 0
for key in clusters:
all_list = clusters[key]
cleaned = list(set(all_list))
if len(cleaned) > 5:
cleaned = sorted(cleaned, key=lambda tup: tup[1]) # 按y值进行排序
avg_y1 = cleaned[0][1]
avg_y2 = cleaned[-1][1]
avg_x1 = 0
avg_x2 = 0
for tup in cleaned:
avg_x1 += tup[0]
avg_x2 += tup[2]
avg_x1 = avg_x1 / len(cleaned)
avg_x2 = avg_x2 / len(cleaned)
rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2)
i += 1
# Step5:画出簇
buff = 7
for key in rects:
tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))
tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))
cv2.rectangle(img, tup_topLeft, tup_botRight, (0, 255, 0), 3)
return img, rects
rect_images = []
rect_coords = []
for img, lines in zip(test_images, list_lines):
new_image, rects = identify_blocks(img, lines)
rect_images.append(new_image)
rect_coords.append(rects)
show(rect_images)
优化定位框
def optimization_rect_coords(rects):
for key in rects:
min_y = 5000
max_y = 0
for rect in key:
if min_y > key[rect][1]:
min_y = key[rect][1]
if max_y < key[rect][3]:
max_y = key[rect][3]
for rect in key:
key[rect] = (key[rect][0], key[rect][1], key[rect][2], max_y - 12)
if rect >= 7:
key[rect] = (key[rect][0], min_y, key[rect][2], key[rect][3])
return rects
def drew(img, optimization_rects):
buff = 7
for key in optimization_rects:
tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))
tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))
cv2.rectangle(img, tup_topLeft, tup_botRight, (0, 255, 0), 3)
return img
optimization_rects = optimization_rect_coords(rect_coords)
optimization_imgs = list(map(drew, test_images, optimization_rects))
show(optimization_imgs)
微调
adj_y1 = {0: -12, 1: -5, 2: 0, 3: 0, 4: 10, 5: 12, 6: -15, 7: -32, 8: -32, 9: -25, 10: 50, 11: 0}
adj_y2 = {0: 0, 1: -5, 2: -3, 3: 0, 4: -1, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 5, 11: 53}
adj_x1 = {0: -8, 1: -10, 2: -10, 3: -10, 4: -15, 5: -15, 6: -15, 7: -15, 8: -15, 9: -10, 10: -10, 11: -2}
adj_x2 = {0: 0, 1: 18, 2: 18, 3: 16, 4: 10, 5: 10, 6: 10, 7: 10, 8: 10, 9: 15, 10: 12, 11: 0}
for key in rects:
tup = rects[key]
x1 = int(tup[0] + adj_x1[key])
x2 = int(tup[2] + adj_x2[key])
y1 = int(tup[1] + adj_y1[key])
y2 = int(tup[3] + adj_y2[key])
cv2.rectangle(new_image, (x1, y1), (x2, y2), color, 2)
蓝色框为微调后定位框
分割
num_splits = int(abs(y2 - y1)//gap) # 分割
for i in range(0, num_splits + 1):
y = int(y1 + i * gap)
cv2.line(new_image, (x1, y), (x2, y), (0, 0, 255), thickness)
if key > 0 and key < len(rects) - 1:
# 竖直线
x = int((x1 + x2) / 2)
cv2.line(new_image, (x, y1), (x, y2), (0, 0, 255), thickness)
tot_spots += (num_splits + 1) * 2
else:
tot_spots += num_splits + 1
字典对应
if key == 0 and key == (len(rects) - 1):
for i in range(0, num_splits + 1):
cur_len = len(spot_dict)
y = int(y1 + i * gap)
spot_dict[(x1, y, x2, y + gap)] = cur_len + 1
else:
for i in range(0, num_splits + 1):
cur_len = len(spot_dict)
y = int(y1 + i * gap)
x = int((x1 + x2) / 2)
spot_dict[(x1, y, x, y + gap)] = cur_len + 1
spot_dict[(x, y, x2, y + gap)] = cur_len + 2
保存参数
import pickle
with open('spot_dict.pickle', 'wb') as handle:
pickle.dump(spot_pos[0], handle, protocol=pickle.HIGHEST_PROTOCOL) # spot_pos[0]定位更精确
构建数据集
import os
def data(img, spot_dict):
# img = np.copy(img)
for spot in spot_dict.keys():
(x1, y1, x2, y2) = spot
(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))
# 裁剪
spot_img = img[y1:y2, x1:x2] * 255
spot_img = cv2.resize(spot_img, (0, 0), fx=2.0, fy=2.0)
spot_id = spot_dict[spot]
filename = 'spot' + str(spot_id) + '.png'
cv2.imwrite(os.path.join('./data', filename), spot_img)
神经网络训练车位是否有车（略）
完整代码
import glob
import cv2 # opencv读取的格式是BGR
import matplotlib.pyplot as plt
import numpy as np
def show(img):
img[0] = cv2.resize(img[0], (900, 600))
img[1] = cv2.resize(img[1], (900, 600))
pic = np.hstack((img[0], img[1]))
cv2.imshow('pic', pic)
cv2.waitKey(0)
cv2.destroyAllWindows()
def select_rgb_white_yellow(img):
# 过滤掉背景
lower = np.float32([0.45, 0.45, 0.45])
upper = np.float32([1.0, 1.0, 1.0])
# 低于lower和高于upper都变成0，其余变成255
white_mask = img.copy()
masked = img.copy()
for i in range(len(img)):
white_mask[i] = cv2.inRange(img[i], lower, upper)
masked[i] = cv2.bitwise_and(img[i], img[i], mask=white_mask[i])
# show(white_mask)
# show(masked)
return masked
def convert_gray_scale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def int8(img):
img = img * 255
return img.astype(np.uint8)
def detect_edges(img, low_th=80, high_th=200):
img = int8(img)
return cv2.Canny(img, low_th, high_th)
def select_region(img):
rows, cols = img.shape[:2]
pt_1 = [cols * 0.05, rows * 0.90]
pt_2 = [cols * 0.05, rows * 0.70]
pt_3 = [cols * 0.30, rows * 0.52]
pt_4 = [cols * 0.60, rows * 0.15]
pt_5 = [cols * 0.90, rows * 0.15]
pt_6 = [cols * 0.90, rows * 0.90]
vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]],dtype=np.int32)
point_img = img.copy()
point_img = cv2.cvtColor(point_img, cv2.COLOR_GRAY2RGB)
for point in vertices[0]:
cv2.circle(point_img, (point[0], point[1]), 10, (0, 0, 255), 4)
return point_img
def filter_region(img):
rows, cols = img.shape[:2]
pt_1 = [cols * 0.05, rows * 0.90]
pt_2 = [cols * 0.05, rows * 0.70]
pt_3 = [cols * 0.30, rows * 0.52]
pt_4 = [cols * 0.60, rows * 0.15]
pt_5 = [cols * 0.90, rows * 0.15]
pt_6 = [cols * 0.90, rows * 0.90]
vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32)
mask = np.zeros_like(img)
if len(mask.shape) == 2:
cv2.fillPoly(mask, vertices, 255) # 填充
return cv2.bitwise_and(img, mask)
def hough_lines(img):
# 输入图像是边缘检测后的图像
return cv2.HoughLinesP(img, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4)
def draw_lines(img, lines, color=[255, 0, 0], thickness=2, make_copy=True):
if make_copy:
img = np.copy(img)
cleaned =[]
for line in lines:
for x1, y1, x2, y2 in line:
if abs(y2 - y1) <=1 and abs(x2 - x1) >= 25 and abs(x2 - x1) <= 55 :
cleaned.append((x1, y1, x2, y2))
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
return img
def identify_blocks(img, lines, make_copy=True):
if make_copy:
img = np.copy(img)
# Step1:过滤直线
cleaned =[]
for line in lines:
for x1, y1, x2, y2 in line:
if abs(y2 - y1) <=1 and abs(x2 - x1) >= 25 and abs(x2 - x1) <= 55 :
cleaned.append((x1, y1, x2, y2))
# Step2:过滤后的直线按x1进行排序
import operator
list1 = sorted(cleaned, key=operator.itemgetter(0, 1))
# Step3:找到多个列
clusters = {}
dIndex = 0
clus_dist = 15
for i in range(len(list1) - 1):
distance = abs(list1[i+1][0] - list1[i][0])
if distance <= clus_dist:
if not dIndex in clusters.keys(): clusters[dIndex] = []
clusters[dIndex].append(list1[i])
clusters[dIndex].append(list1[i + 1]) # 会重复加入字典
else:
dIndex += 1
# Step4:得到坐标
rects = {}
i = 0
for key in clusters:
all_list = clusters[key]
cleaned = list(set(all_list))
if len(cleaned) > 5:
cleaned = sorted(cleaned, key=lambda tup: tup[1]) # 按y值进行排序
avg_y1 = cleaned[0][1]
avg_y2 = cleaned[-1][1]
avg_x1 = 0
avg_x2 = 0
for tup in cleaned:
avg_x1 += tup[0]
avg_x2 += tup[2]
avg_x1 = avg_x1 / len(cleaned)
avg_x2 = avg_x2 / len(cleaned)
rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2)
i += 1
# Step5:画出簇
buff = 7
for key in rects:
tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))
tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))
cv2.rectangle(img, tup_topLeft, tup_botRight, (0, 255, 0), 3)
return img, rects
def optimization_rect_coords(rects):
for key in rects:
min_y = 5000
max_y = 0
for rect in key:
if min_y > key[rect][1]:
min_y = key[rect][1]
if max_y < key[rect][3]:
max_y = key[rect][3]
for rect in key:
key[rect] = (key[rect][0], key[rect][1], key[rect][2], max_y - 12)
if rect >= 7:
key[rect] = (key[rect][0], min_y, key[rect][2], key[rect][3])
return rects
def drew(img, optimization_rects):
buff = 7
img = np.copy(img)
for key in optimization_rects:
tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))
tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))
cv2.rectangle(img, tup_topLeft, tup_botRight, (0, 255, 0), 2)
return img
def draw_parking(image, rects, make_copy=True, color=[255, 0, 0], thickness=2, save=True):
if make_copy:
new_image = np.copy(image)
gap = 16.0
spot_dict = {} # 车位对应字典
tot_spots = 0
# 微调
adj_y1 = {0: -12, 1: -5, 2: 0, 3: 0, 4: 10, 5: 12, 6: -15, 7: -32, 8: -32, 9: -25, 10: 50, 11: 0}
adj_y2 = {0: 0, 1: -5, 2: -3, 3: 0, 4: -1, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 5, 11: 53}
adj_x1 = {0: -8, 1: -10, 2: -10, 3: -10, 4: -15, 5: -15, 6: -15, 7: -15, 8: -15, 9: -10, 10: -10, 11: -2}
adj_x2 = {0: 0, 1: 18, 2: 18, 3: 16, 4: 10, 5: 10, 6: 10, 7: 10, 8: 10, 9: 15, 10: 12, 11: 0}
for key in rects:
tup = rects[key]
x1 = int(tup[0] + adj_x1[key])
x2 = int(tup[2] + adj_x2[key])
y1 = int(tup[1] + adj_y1[key])
y2 = int(tup[3] + adj_y2[key])
cv2.rectangle(new_image, (x1, y1), (x2, y2), color, 2)
num_splits = int(abs(y2 - y1)//gap) # 分割
for i in range(0, num_splits + 1):
y = int(y1 + i * gap)
cv2.line(new_image, (x1, y), (x2, y), (0, 0, 255), thickness)
if key > 0 and key < len(rects) - 1:
# 竖直线
x = int((x1 + x2) / 2)
cv2.line(new_image, (x, y1), (x, y2), (0, 0, 255), thickness)
tot_spots += (num_splits + 1) * 2
else:
tot_spots += num_splits + 1
# 字典对应
if key == 0 and key == (len(rects) - 1):
for i in range(0, num_splits + 1):
cur_len = len(spot_dict)
y = int(y1 + i * gap)
spot_dict[(x1, y, x2, y + gap)] = cur_len + 1
else:
for i in range(0, num_splits + 1):
cur_len = len(spot_dict)
y = int(y1 + i * gap)
x = int((x1 + x2) / 2)
spot_dict[(x1, y, x, y + gap)] = cur_len + 1
spot_dict[(x, y, x2, y + gap)] = cur_len + 2
return new_image, spot_dict
import os
def data(img, spot_dict):
# img = np.copy(img)
for spot in spot_dict.keys():
(x1, y1, x2, y2) = spot
(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))
# 裁剪
spot_img = img[y1:y2, x1:x2] * 255
spot_img = cv2.resize(spot_img, (0, 0), fx=2.0, fy=2.0)
spot_id = spot_dict[spot]
filename = 'spot' + str(spot_id) + '.png'
cv2.imwrite(os.path.join('./data', filename), spot_img)
test_images = [plt.imread(path) for path in glob.glob('test_images/*.png')]
class_dictionary = {}
class_dictionary[0] = 'empty'
class_dictionary[1] = 'occupied'
# show(test_images)
# 过滤背景
images = select_rgb_white_yellow(test_images)
# show(images)
# 转灰度图
gray_images = list(map(convert_gray_scale, images))
# show(gray_images)
# 边缘检测
edge_images = list(map(detect_edges, gray_images))
# show(edge_images)
# 定位区域
roi_images = list(map(select_region, edge_images))
# show(roi_images)
# 剔除多余区域
region_images = list(map(filter_region, edge_images))
# show(region_images)
# 检测直线
list_lines = list(map(hough_lines, region_images))
# print(len(list_lines[0]))
# 画线
line_images = []
for img, lines in zip(test_images, list_lines):
line_images.append(draw_lines(img, lines))
# show(line_images)
# 定位列
rect_images = []
rect_coords = []
for img, lines in zip(test_images, list_lines):
new_image, rects = identify_blocks(img, lines)
rect_images.append(new_image)
rect_coords.append(rects)
# show(rect_images)
# 优化定位框
optimization_rects = optimization_rect_coords(rect_coords)
optimization_imgs = list(map(drew, test_images, optimization_rects))
# show(optimization_imgs)
# 切分停车位
delineated = []
spot_pos = []
for image, rects in zip(test_images, optimization_rects):
new_image, spot_dict = draw_parking(image, rects)
data(image, spot_dict) # 数据集构建
delineated.append(new_image)
spot_pos.append(spot_dict)
# show(delineated)
# 保存
import pickle
with open('spot_dict.pickle', 'wb') as handle:
pickle.dump(spot_pos[0], handle, protocol=pickle.HIGHEST_PROTOCOL) # spot_pos[0]定位更精确