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detect_plate.py
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detect_plate.py
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# -*- coding: UTF-8 -*-
import argparse
import time
import os
import cv2
import torch
import copy
import numpy as np
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import check_img_size, non_max_suppression_face, scale_coords
from utils.cv_puttext import cv2ImgAddText
from plate_recognition.plate_rec import get_plate_result, allFilePath, init_model, cv_imread
from plate_recognition.double_plate_split_merge import get_split_merge
clors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
danger = ['危', '险']
def order_points(pts): # 四个点按照左上 右上 右下 左下排列
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def four_point_transform(image, pts): # 透视变换得到车牌小图
rect = order_points(pts)
(tl, tr, br, bl) = rect
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
return warped
def load_model(weights, device):
model = attempt_load(weights, map_location=device) # load FP32 model
return model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): # 返回到原图坐标
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2, 4, 6]] -= pad[0] # x padding
coords[:, [1, 3, 5, 7]] -= pad[1] # y padding
coords[:, :10] /= gain
# clip_coords(coords, img0_shape)
coords[:, 0].clamp_(0, img0_shape[1]) # x1
coords[:, 1].clamp_(0, img0_shape[0]) # y1
coords[:, 2].clamp_(0, img0_shape[1]) # x2
coords[:, 3].clamp_(0, img0_shape[0]) # y2
coords[:, 4].clamp_(0, img0_shape[1]) # x3
coords[:, 5].clamp_(0, img0_shape[0]) # y3
coords[:, 6].clamp_(0, img0_shape[1]) # x4
coords[:, 7].clamp_(0, img0_shape[0]) # y4
# coords[:, 8].clamp_(0, img0_shape[1]) # x5
# coords[:, 9].clamp_(0, img0_shape[0]) # y5
return coords
def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num, device, plate_rec_model, is_color=False):
h, w, c = img.shape
result_dict = {}
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
height = y2 - y1
landmarks_np = np.zeros((4, 2))
rect = [x1, y1, x2, y2]
for i in range(4):
point_x = int(landmarks[2 * i])
point_y = int(landmarks[2 * i + 1])
landmarks_np[i] = np.array([point_x, point_y])
class_label = int(class_num) # 车牌的的类型0代表单牌,1代表双层车牌
roi_img = four_point_transform(img, landmarks_np) # 透视变换得到车牌小图
if class_label: # 判断是否是双层车牌,是双牌的话进行分割后然后拼接
roi_img = get_split_merge(roi_img)
if not is_color:
plate_number, rec_prob = get_plate_result(roi_img, device, plate_rec_model, is_color=is_color) # 对车牌小图进行识别
else:
plate_number, rec_prob, plate_color, color_conf = get_plate_result(roi_img, device, plate_rec_model,
is_color=is_color)
for dan in danger: # 只要出现‘危’或者‘险’就是危险品车牌
if dan in plate_number:
plate_number = '危险品'
# cv2.imwrite("roi.jpg",roi_img)
result_dict['rect'] = rect # 车牌roi区域
result_dict['detect_conf'] = conf # 检测区域得分
result_dict['landmarks'] = landmarks_np.tolist() # 车牌角点坐标
result_dict['plate_no'] = plate_number # 车牌号
result_dict['rec_conf'] = rec_prob # 每个字符的概率
result_dict['roi_height'] = roi_img.shape[0] # 车牌高度
result_dict['plate_color'] = ""
if is_color:
result_dict['plate_color'] = plate_color # 车牌颜色
result_dict['color_conf'] = color_conf
result_dict['plate_type'] = class_label # 单双层 0单层 1双层
return result_dict
def detect_Recognition_plate(model, orgimg, device, plate_rec_model, img_size, is_color=False):
# Load model
# img_size = opt_img_size
conf_thres = 0.3
iou_thres = 0.5
dict_list = []
# orgimg = cv2.imread(image_path) # BGR
img0 = copy.deepcopy(orgimg)
assert orgimg is not None, 'Image Not Found '
h0, w0 = orgimg.shape[:2] # orig hw
r = img_size / max(h0, w0) # resize image to img_size
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)
imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size
img = letterbox(img0, new_shape=imgsz)[0]
# img =process_data(img0)
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416
# Run inference
t0 = time.time()
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
# t1 = time_synchronized()/
pred = model(img)[0]
# t2=time_synchronized()
# print(f"infer time is {(t2-t1)*1000} ms")
# Apply NMS
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
# print('img.shape: ', img.shape)
# print('orgimg.shape: ', orgimg.shape)
# Process detections
for i, det in enumerate(pred): # detections per image
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round()
for j in range(det.size()[0]):
xyxy = det[j, :4].view(-1).tolist()
conf = det[j, 4].cpu().numpy()
landmarks = det[j, 5:13].view(-1).tolist()
class_num = det[j, 13].cpu().numpy()
result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num, device, plate_rec_model,
is_color=is_color)
dict_list.append(result_dict)
return dict_list
# cv2.imwrite('result.jpg', orgimg)
def draw_result(orgimg, dict_list, is_color=False):
result_str = ""
for result in dict_list:
rect_area = result['rect']
x, y, w, h = rect_area[0], rect_area[1], rect_area[2] - rect_area[0], rect_area[3] - rect_area[1]
padding_w = 0.05 * w
padding_h = 0.11 * h
rect_area[0] = max(0, int(x - padding_w))
rect_area[1] = max(0, int(y - padding_h))
rect_area[2] = min(orgimg.shape[1], int(rect_area[2] + padding_w))
rect_area[3] = min(orgimg.shape[0], int(rect_area[3] + padding_h))
height_area = result['roi_height']
landmarks = result['landmarks']
result_p = result['plate_no']
if result['plate_type'] == 0: # 单层
result_p += " " + result['plate_color']
else: # 双层
result_p += " " + result['plate_color'] + "双层"
result_str += result_p + " "
for i in range(4): # 关键点
cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
cv2.rectangle(orgimg, (rect_area[0], rect_area[1]), (rect_area[2], rect_area[3]), (0, 0, 255), 2) # 画框
if len(result) >= 1:
if "危险品" in result_p: # 如果是危险品车牌,文字就画在下面
orgimg = cv2ImgAddText(orgimg, result_p, rect_area[0], rect_area[3], (0, 255, 0), height_area)
else:
orgimg = cv2ImgAddText(orgimg, result_p, rect_area[0] - height_area, rect_area[1] - height_area - 10,
(0, 255, 0), height_area)
print(result_str)
return orgimg
def get_second(capture):
if capture.isOpened():
rate = capture.get(5) # 帧速率
FrameNumber = capture.get(7) # 视频文件的帧数
duration = FrameNumber / rate # 帧速率/视频总帧数 是时间,除以60之后单位是分钟
return int(rate), int(FrameNumber), int(duration)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--detect_model', nargs='+', type=str, default='weights/plate_detect.pt',
help='model.pt path(s)') # 检测模型
parser.add_argument('--rec_model', type=str, default='weights/plate_rec_color.pth',
help='model.pt path(s)') # 车牌识别+颜色识别模型
parser.add_argument('--is_color', type=bool, default=True, help='plate color') # 是否识别颜色
parser.add_argument('--image_path', type=str, default='imgs', help='source')
parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--output', type=str, default='result1', help='source')
parser.add_argument('--video', type=str, default='', help='source')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device =torch.device("cpu")
opt = parser.parse_args()
print(opt)
save_path = opt.output
count = 0
if not os.path.exists(save_path):
os.mkdir(save_path)
detect_model = load_model(opt.detect_model, device) # 初始化检测模型
plate_rec_model = init_model(device, opt.rec_model, is_color=opt.is_color) # 初始化识别模型
# 算参数量
total = sum(p.numel() for p in detect_model.parameters())
total_1 = sum(p.numel() for p in plate_rec_model.parameters())
print("detect params: %.2fM,rec params: %.2fM" % (total / 1e6, total_1 / 1e6))
time_all = 0
time_begin = time.time()
if not opt.video: # 处理图片
if not os.path.isfile(opt.image_path): # 目录
file_list = []
allFilePath(opt.image_path, file_list)
for img_path in file_list:
print(count, img_path, end=" ")
time_b = time.time()
img = cv_imread(img_path)
if img is None:
continue
if img.shape[-1] == 4:
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
# detect_one(model,img_path,device)
dict_list = detect_Recognition_plate(detect_model, img, device, plate_rec_model, opt.img_size,
is_color=opt.is_color)
ori_img = draw_result(img, dict_list)
img_name = os.path.basename(img_path)
save_img_path = os.path.join(save_path, img_name)
time_e = time.time()
time_gap = time_e - time_b
if count:
time_all += time_gap
cv2.imwrite(save_img_path, ori_img)
count += 1
print(
f"sumTime time is {time.time() - time_begin} s, average pic time is {time_all / (len(file_list) - 1)}")
else: # 单个图片
print(count, opt.image_path, end=" ")
img = cv_imread(opt.image_path)
if img.shape[-1] == 4:
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
dict_list = detect_Recognition_plate(detect_model, img, device, plate_rec_model, opt.img_size,
is_color=opt.is_color)
ori_img = draw_result(img, dict_list)
img_name = os.path.basename(opt.image_path)
save_img_path = os.path.join(save_path, img_name)
cv2.imwrite(save_img_path, ori_img)
else: # 处理视频
video_name = opt.video
capture = cv2.VideoCapture(video_name)
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
fps = capture.get(cv2.CAP_PROP_FPS) # 帧数
width, height = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 宽高
out = cv2.VideoWriter('result.mp4', fourcc, fps, (width, height)) # 写入视频
frame_count = 0
fps_all = 0
rate, FrameNumber, duration = get_second(capture)
if capture.isOpened():
while True:
t1 = cv2.getTickCount()
frame_count += 1
print(f"第{frame_count} 帧", end=" ")
ret, img = capture.read()
if not ret:
break
img0 = copy.deepcopy(img)
dict_list = detect_Recognition_plate(detect_model, img, device, plate_rec_model, opt.img_size,
is_color=opt.is_color)
ori_img = draw_result(img, dict_list)
t2 = cv2.getTickCount()
infer_time = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / infer_time
fps_all += fps
str_fps = f'fps:{fps:.4f}'
cv2.putText(ori_img, str_fps, (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
out.write(ori_img)
else:
print("失败")
capture.release()
out.release()
cv2.destroyAllWindows()
print(f"all frame is {frame_count},average fps is {fps_all / frame_count} fps")