import face_recognition from PIL import Image, ImageDraw,ImageFont import cv2 import numpy as np
时间: 2023-11-13 18:05:31 浏览: 183
这段代码缺少了一些语法,可能会出现错误。但我可以尝试解释一下这段代码的作用:
这段代码主要是用于人脸识别和绘制人脸边框的。其中使用了 face_recognition 库来进行人脸识别,使用了 PIL 库进行图像绘制,使用了 cv2 库进行图像读取和显示,使用了 numpy 库进行数据处理。
具体的操作流程大概是这样的:
1. 读取图像文件
2. 使用 face_recognition 库对图像中的人脸进行定位
3. 使用 PIL 库在图像上绘制人脸边框
4. 使用 cv2 库将带有边框的图像显示出来
需要注意的是,这段代码没有完整的函数或类定义,可能只是作为一个脚本文件来使用。如果要正常运行,可能需要从其他地方引用一些变量或函数。
相关问题
import cv2 import face_recognition import numpy as np from PIL import Image, ImageDraw,ImageFont video_capture = cv2.VideoCapture(r'C:/Users/ALIENWARE/123.mp4')#如果输入是(0)为摄像头输入 #现输入为MP4进行识别检测人脸 first_image = face_recognition.load_image_file("1.jpg") first_face_encoding = face_recognition.face_encodings(first_image)[0] Second_image = face_recognition.load_image_file("2.jpg") Second_face_encoding = face_recognition.face_encodings(Second_image)[0] third_image = face_recognition.load_image_file("3.jpg") third_face_encoding = face_recognition.face_encodings(third_image)[0] inside_face_encodings = [first_face_encoding,Second_face_encoding,third_face_encoding] inside_face_names = ['A','B','C'] face_locations = [] face_encodings = [] face_names = [] process_this_frame = True while True: ret, frame = video_capture.read() small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) rgb_small_frame = small_frame[:, :, ::-1] if process_this_frame: face_locations = face_recognition.face_locations(rgb_small_frame) face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations) face_names = [] for face_encoding in face_encodings: matches = face_recognition.compare_faces(inside_face_encodings, face_encoding) name = '未录入人脸' if True in matches: first_match_index = matches.index(True) name = inside_face_names[first_match_index] face_names.append(name) process_this_frame = not process_this_frame for (top, right, bottom, left), name in zip(face_locations, face_names): top *= 4 right *= 4 bottom *= 4 left *= 4 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) img_pil = Image.fromarray(frame) draw = ImageDraw.Draw(img_pil) fontStyle = ImageFont.truetype("C:/Windows/Fonts/simsun.ttc", 32, encoding="utf-8") draw.text((left + 6, bottom - 6), name, (0, 200, 0), font=fontStyle) frame = np.asarray(np.array(img_pil)) cv2.imshow('face_out', frame) if cv2.waitKey(1) & 0xFF == ord('q'): #退出需要按下Q键否则内核会崩溃 break video_capture.release() cv2.destroyAllWindows()
以下是修改后的代码,主要是添加了注释和一些规范化的修改:
```python
import cv2
import face_recognition
import numpy as np
from PIL import Image, ImageDraw, ImageFont
# 加载要识别的人脸图片
first_image = face_recognition.load_image_file("1.jpg")
first_face_encoding = face_recognition.face_encodings(first_image)[0]
Second_image = face_recognition.load_image_file("2.jpg")
Second_face_encoding = face_recognition.face_encodings(Second_image)[0]
third_image = face_recognition.load_image_file("3.jpg")
third_face_encoding = face_recognition.face_encodings(third_image)[0]
# 存储要检测的人脸编码及对应的人名
inside_face_encodings = [first_face_encoding, Second_face_encoding, third_face_encoding]
inside_face_names = ['A', 'B', 'C']
# 打开视频流
video_capture = cv2.VideoCapture(r'C:/Users/ALIENWARE/123.mp4')
# 循环读取视频帧
while True:
# 读取一帧
ret, frame = video_capture.read()
# 降低帧率,加快处理速度
if not ret:
break
# 缩小图片尺寸,加快处理速度
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
# 处理每一帧图片
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
# 遍历每个检测到的人脸
for face_encoding in face_encodings:
# 比对人脸编码
matches = face_recognition.compare_faces(inside_face_encodings, face_encoding)
name = '未录入人脸'
# 找到匹配的人脸
if True in matches:
first_match_index = matches.index(True)
name = inside_face_names[first_match_index]
face_names.append(name)
# 在画面上标记人脸及名字
for (top, right, bottom, left), name in zip(face_locations, face_names):
# 将坐标放大4倍,因为之前缩小了4倍
top *= 4
right *= 4
bottom *= 4
left *= 4
# 画出人脸框
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# 添加名字
img_pil = Image.fromarray(frame)
draw = ImageDraw.Draw(img_pil)
fontStyle = ImageFont.truetype("C:/Windows/Fonts/simsun.ttc", 32, encoding="utf-8")
draw.text((left + 6, bottom - 6), name, (0, 200, 0), font=fontStyle)
frame = np.asarray(np.array(img_pil))
# 显示画面
cv2.imshow('face_out', frame)
# 按下q键退出程序
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放视频流
video_capture.release()
# 关闭所有窗口
cv2.destroyAllWindows()
```
from hyperlpr import * # 导入OpenCV库 import cv2 as cv from PIL import Image, ImageDraw, ImageFont import numpy as np def drawRectBox(image, rect, addText, fontC): cv.rectangle(image, (int(round(rect[0])), int(round(rect[1]))), (int(round(rect[2]) + 8), int(round(rect[3]) + 8)), (0, 0, 255), 2) cv.rectangle(image, (int(rect[0] - 1), int(rect[1]) - 16), (int(rect[0] + 75), int(rect[1])), (0, 0, 255), -1, cv.LINE_AA) img = Image.fromarray(image) draw = ImageDraw.Draw(img) draw.text((int(rect[0] + 1), int(rect[1] - 16)), addText, (255, 255, 255), font=fontC) imagex = np.array(img) return imagex image = cv.imread('./img/test3.jpeg') # 读取选择的图片 res_all = HyperLPR_plate_recognition(image) fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0) res, confi, axes = res_all[0] image = drawRectBox(image, axes, res, fontC) cv.imshow('Stream', image) c = cv.waitKey(0) & 0xff
//只检测到电感3未检测到黑线
for(i=0;i<4;i++) //电机1正转,电机2反转
{
P0 = table[i];
IN1 = 0;
IN这是一段 Python 代码,用于从指定的图片中读取并识别车牌,并在图像上绘2 = 1;
IN3 = 1;
IN4 = 0;
ENA = 1;
EN制车牌的边框和标注文字。以下是代码的主要功能:
1. 导入必要的库和模B = 1;
delay(100);
}
break;
case 0x0c: //只检测块,如 HyperLPR、OpenCV、Pillow 和 NumPy。
2. 定义了一个 'drawRectBox' 函数,到电感4未检测到黑线
for(i=0;i<4;i++) //电机1反转,用于在图像上绘制车牌的边框和标注文字。
3. 读取指定的图片,并使用电机2正转
{
P0 = table[i];
IN1 = 1;
IN2 = 0 HyperLPR 库进行车牌识别。
4. 获取识别结果,包括车牌号码、置信度和车;
IN3 = 0;
IN4 = 1;
ENA = 1;
ENB = 1牌在图像中的位置。
5. 使用 'drawRectBox' 函数在图像上绘制车牌的边框和;
delay(100);
}
break;
case 0x0e: //只检测到电感5标注文字,并将处理后的图像显示出来。
6. 等待用户按下任意键,然后退出程序未检测到黑线
for(i=0;i<4;i++) //电机1反转,电机2正。
请注意,要运行此代码,您需要先安装 HyperLPR 和其他必要的库和模块,并且转
{
P0 = table[i];
IN1 = 1;
IN2 = 0;
IN3需要将字体文件 'platech.ttf' 放置在正确的位置。另外,您需要将指定的图片文件 ' = 0;
IN4 = 1;
ENA = 1;
ENB = 1;
delay(test3.jpeg' 放置在正确的位置。
阅读全文