def evaluate_model(trained_model, data_loader, netname='CrowdCounter'):
if (netname is 'CrowdCounter'):
net = CrowdCounter()
elif (netname is 'MCNNNet'):
net = MCNNNet()
elif (netname is 'AmendNet'):
net = AmendNet()
elif (netname is 'CrowdCounter_MSCNN'):
net = CrowdCounter_MSCNN()
else:
raise (
'netname should be one of ["CrowdCounter", "MCNNNet", "AmendNet", "CrowdCounter_MSCNN"],\
but we got ', netname)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
mae += abs(gt_count - et_count)
mse += ((gt_count - et_count) * (gt_count - et_count))
mae = mae / data_loader.get_num_samples()
mse = np.sqrt(mse / data_loader.get_num_samples())
return mae, mse
def evaluate_model(trained_model, data_loader):
net = CrowdCounter()
network.load_net(trained_model, net)
# net.cuda()
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
mae += abs(gt_count - et_count)
mse += ((gt_count - et_count) * (gt_count - et_count))
mae = mae / data_loader.get_num_samples()
mse = np.sqrt(mse / data_loader.get_num_samples())
return mae, mse
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
mape = 0.0
total_count = 0.0
#load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
def detect(per_img, back_img, threshold, model_path, scene):
# 用于存储最终结果
result_dic = {}
person_list = []
total_person = 0
police_num = 0
prisoner_num = 0
other = 0
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
vis = False
save_output = True
model_name = os.path.basename(model_path).split('.')[0]
# 初始化网络
net = CrowdCounter()
trained_model = os.path.join(model_path)
# 加载权值文件
network.load_net(trained_model, net)
# net.cuda() 用于进行cuda加速
net.eval()
# 读取图片
img_r = cv2.imread(per_img)
img1 = cv2.imread(per_img, 0)
# 保存灰度化之后的图片
# img_gray = img1
img = img1.astype(np.float32, copy=False)
ht = img.shape[0]
wd = img.shape[1]
ht_1 = int((ht / 4) * 4)
wd_1 = int((wd / 4) * 4)
img = cv2.resize(img, (wd_1, ht_1))
img = img.reshape((1, 1, img.shape[0], img.shape[1]))
# 对图片进行预测
heat_map = net.forward(img)
heat_map = torch.squeeze(heat_map)
heat_map = heat_map.data.numpy()
[height, width] = heat_map.shape
dst = cv2.resize(img_r, (width, height), interpolation=cv2.INTER_LINEAR)
# img_gray = cv2.resize(img_gray, (width, height), interpolation=cv2.INTER_LINEAR)
# 计算背景图和目标图片的梯度
hist1 = gradient(per_img, width, height)
hist2 = gradient(back_img, width, height)
result = np.empty([height, width])
# 计算梯度差
for i in range(width - 1):
for j in range(height - 1):
result[j, i] = abs(hist1[j, i] - hist2[j, i])
# 输出提督差结果
# for i in range(width - 1):
# for j in range(height - 1):
# print('{0}-{1},{2}'.format(result[j, i], i, j))
# 根据梯度差得出目标区域
l = []
for i in range(width - 1):
for j in range(height - 1):
# print('{0}-({1},{2})'.format(result[j, i], j, i))
count = 0
if heat_map[j, i] > 0: # 预测结果大于0即可能是人
# dst = cv2.circle(dst, (i, j), 1, (0, 0, 255), -1)
# 将目标点向左向右个扩充15个像素向下扩充50个像素对比梯度差
for ix in range(30):
for jy in range(50):
if (jy + j > 0) and (jy + j < height - 1) and (
ix + i - 5 > 0) and (ix + i - 5 < width - 1):
if result[jy + j, ix + i - 15] > threshold:
# 输出满足条件的点的阈值和坐标
count += 1
# 根据图片的大小对左下角和右上角文字区域进行重点过滤
if height == 180 and width == 320:
if j > 22 and (i < 175 or j < 156) and (i < 276
or j < 127):
if count > 500:
l.append([i, j])
# 在满足条件的地方绘制十字
# dst = cv2.circle(dst, (i, j), 1, (0, 0, 255), -1)
elif height == 144 and width == 176:
if (i > 87 or j > 12) and (i < 116
or j < 122) and (i < 148
or j < 103):
if count > 500:
l.append([i, j])
# dst = cv2.circle(dst, (i, j), 1, (0, 0, 255), -1)
else:
if j > 22 and (i < 175 or j < 156) and (i < 276
or j < 127):
if count > 500:
l.append([i, j])
# 在满足条件的地方绘制十字
# dst = cv2.circle(dst, (i, j), 1, (0, 0, 255), -1)
rects = []
entry = True
x = 0
y = 0
old_x = 0
old_y = 0
hsv = cv2.cvtColor(dst, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
lower_blue = np.array([110, 100, 50])
upper_blue = np.array([130, 255, 255])
lower_black = np.array([0, 0, 0])
upper_black = np.array([170, 255, 48])
mask1 = cv2.inRange(hsv, lower_blue, upper_blue)
mask2 = cv2.inRange(hsv, lower_black, upper_black)
# 将结果与原图做按位与
# blueThings = cv2.bitwise_and(dst, dst, mask=mask)
# 对目标区域通过矩形框进行聚类
# 走廊/ab门
if scene == 1:
for (i, j) in l:
if entry:
# cv2.rectangle(dst, (i - 10, j - 10), (i + 20, j + 40), (0, 255, 0), 1)
rects.append([i - 10, j - 10, 30, 35])
x = i + 10
y = j + 20
old_x = i
old_y = j
entry = False
if i > x or (j - y) > 35 or (old_y - j > 15
and abs(old_x - i) < 15):
entry = True
# else:
# if j < old_y:
# rects.pop()
# rects.append([i - 10, j - 10, 30, 35])
# 对图片进行二值化
# ret, thresh1 = cv2.threshold(img_gray, 75, 255, cv2.THRESH_BINARY)
# cv2.imshow("binary", thresh1)
# thresh1 = thresh1.astype(np.uint32, copy=False) # for i in range(width - 1):
# for j in range(height - 1):
# print('{0}-({1},{2})'.format(thresh1[j, i], i, j))
# 对矩形框区域使用梯度差进行再次过滤
l2 = []
# 通过颜色判断人物的角色
# 黑色：警察
# 蓝色：囚犯
# 其他：普通人
for (x, y, w, h) in rects:
count = 0
# 保存矩形框类的黑色和蓝色区域
black_count = 0
blue_count = 0
# 用于标示是否已添加文字类别
label_entry = True
for i in range(w - 1):
for j in range(h - 1):
# 人员信息
person_dic = {}
if mask1[y + j, x + i] == 255:
blue_count += 1
if mask2[y + j, x + i] == 255:
black_count += 1
# if thresh1[y + j, x + i] == 255:
# thresh_count = thresh_count + 1
if result[y + j, x + i] > threshold:
count += 1
if count > 350: # and thresh_count < (w * h)/3:
l2.append([x, y, w, h])
# 根据颜色阈值对警察和囚犯进行标记
if label_entry:
if blue_count > 50:
total_person += 1 # 用于计数总人数
prisoner_num += 1 # 用于计数囚犯数目
label_entry = False
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'prisoner'
cv2.rectangle(dst, (x, y), (x + w, y + h),
(0, 0, 255), 1)
cv2.putText(dst, 'prisoner', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(0, 0, 255), 1)
person_list.append(person_dic)
elif black_count > 170:
total_person += 1 # 用于计数总人数
police_num += 1 # 用于计数警察数目
label_entry = False
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'police'
cv2.rectangle(dst, (x, y), (x + w, y + h),
(255, 0, 0), 1)
cv2.putText(dst, 'police', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 0, 0), 1)
person_list.append(person_dic)
# 人员信息
person_dic = {}
if count > 350 and label_entry:
label_entry = False
total_person += 1 # 用于计数总人数
other += 1 # 用于计数普通人数目
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'other'
cv2.rectangle(dst, (x, y), (x + w, y + h), (255, 255, 0), 1)
cv2.putText(dst, 'other', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1)
person_list.append(person_dic)
# 会见室
elif scene == 2:
l.reverse()
for (i, j) in l:
if entry:
# cv2.rectangle(dst, (i - 10, j - 10), (i + 20, j + 25), (0, 255, 0), 1)
rects.append([i - 10, j - 10, 30, 35])
x = i + 20
y = j + 20
entry = False
if i > x or j > y:
entry = True
# 对矩形框区域使用梯度差进行再次过滤
l2 = []
for (x, y, w, h) in rects:
count = 0
# 保存矩形框类的黑色和蓝色区域
black_count = 0
blue_count = 0
# 用于标示是否已添加文字类别
label_entry = True
for i in range(w - 1):
for j in range(h - 1):
# 人员信息
person_dic = {}
if mask1[y + j, x + i] == 255:
blue_count += 1
if mask2[y + j, x + i] == 255:
black_count += 1
if result[y + j, x + i] > threshold:
count += 1
if count > 420:
l2.append([x, y, w, h])
if label_entry:
if blue_count > 50:
total_person += 1 # 用于计数总人数
prisoner_num += 1 # 用于计数囚犯数目
label_entry = False
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'prisoner'
cv2.rectangle(dst, (x, y), (x + w, y + h),
(0, 0, 255), 1)
cv2.putText(dst, 'prisoner', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(0, 0, 255), 1)
person_list.append(person_dic)
elif black_count > 170:
label_entry = False
total_person += 1 # 用于计数总人数
police_num += 1 # 用于计数警察数目
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'police'
cv2.rectangle(dst, (x, y), (x + w, y + h),
(255, 0, 0), 1)
cv2.putText(dst, 'police', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 0, 0), 1)
person_list.append(person_dic)
# 人员信息
person_dic = {}
if count > 420 and label_entry:
label_entry = False
total_person += 1 # 用于计数总人数
other += 1 # 用于计数普通人数目
person_dic['position'] = [x, y, x + w, y + h]
person_dic['role'] = 'other'
cv2.rectangle(dst, (x, y), (x + w, y + h), (255, 255, 0), 1)
cv2.putText(dst, 'other', (x, y + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1)
person_list.append(person_dic)
print(count)
# 组装生成最后的结果
result_dic['total_people'] = total_person
result_dic['police_num'] = police_num
result_dic['prisoner_num'] = prisoner_num
result_dic['other'] = other
result_dic['people_list'] = person_list
print(result_dic)
cv2.namedWindow("result")
cv2.imshow("result", dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
return result_dic