mp.solutions.face_detection
时间: 2024-04-07 19:30:36 浏览: 3
mp.solutions.face_detection 是指使用 Mediapipe 框架中的 face_detection 模块进行人脸检测。Mediapipe 是一个开源的跨平台框架,用于构建基于机器学习的应用程序。它提供了许多预训练的模型和工具,方便开发者进行各种计算机视觉和机器学习任务。在 face_detection 模块中,可以使用 Mediapipe 提供的 API 进行实时人脸检测,获取人脸位置、关键点等信息。具体的使用方法和代码实现可以参考 Mediapipe 的官方文档和示例代码。
相关问题
import cv2 import mediapipe as mp mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles mp_holistic = mp.solutions.holistic # For webcam input: filepath='F:\\video000\\ce.mp4' cap = cv2.VideoCapture(filepath) with mp_holistic.Holistic( min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: while cap.isOpened(): success, image = cap.read() if not success: print("Ignoring empty camera frame.") # If loading a video, use 'break' instead of 'continue'. break # To improve performance, optionally mark the image as not writeable to # pass by reference. image.flags.writeable = False image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic.process(image) # Draw landmark annotation on the image. image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) mp_drawing.draw_landmarks( image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, landmark_drawing_spec=None, connection_drawing_spec=mp_drawing_styles .get_default_face_mesh_contours_style()) mp_drawing.draw_landmarks( image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec=mp_drawing_styles .get_default_pose_landmarks_style()) # Flip the image horizontally for a selfie-view display. cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1)) if cv2.waitKey(5) & 0xFF == 27: break cap.release() 在这段代码中引入注意力机制,使人体步态特征更好的提取
在这段代码中,要引入注意力机制以提取人体步态特征,可以考虑在Holistic模型的输出结果中使用注意力机制进行加权融合,以强化对关键信息的关注和捕捉。具体操作如下:
1. 在Holistic模型的process()函数中,设置use_attention=True,以启用注意力机制。
2. 在Holistic模型的输出结果中,选择关键的特征点或特征向量,并使用注意力机制进行加权融合,以得到最终的人体步态特征表示。
3. 对特征表示进行进一步的处理和分类,以实现步态识别等应用。
下面是一个示例代码,用于在Holistic模型中引入注意力机制:
```
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_holistic = mp.solutions.holistic
# For webcam input:
filepath = 'F:\\video000\\ce.mp4'
cap = cv2.VideoCapture(filepath)
with mp_holistic.Holistic(
min_detection_confidence=0.5,
min_tracking_confidence=0.5,
use_attention=True) as holistic:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
break
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = holistic.process(image)
# Extract the key feature points or vectors from the output results.
pose_landmarks = results.pose_landmarks.landmark
face_landmarks = results.face_landmarks.landmark
left_hand_landmarks = results.left_hand_landmarks.landmark
right_hand_landmarks = results.right_hand_landmarks.landmark
# Apply attention mechanism to the key feature points or vectors.
pose_attention = apply_attention(pose_landmarks)
face_attention = apply_attention(face_landmarks)
left_hand_attention = apply_attention(left_hand_landmarks)
right_hand_attention = apply_attention(right_hand_landmarks)
# Combine the attention-weighted feature vectors to form the final gait feature.
gait_feature = np.concatenate([pose_attention, face_attention, left_hand_attention, right_hand_attention])
# Further process and classify the gait feature to achieve gait recognition.
...
# Draw landmark annotation on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
mp_drawing.draw_landmarks(
image,
results.face_landmarks,
mp_holistic.FACEMESH_CONTOURS,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles
.get_default_face_mesh_contours_style())
mp_drawing.draw_landmarks(
image,
results.pose_landmarks,
mp_holistic.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles
.get_default_pose_landmarks_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
```
其中,apply_attention()函数用于对输入的特征点或特征向量应用注意力机制,可以根据具体需求选择不同的注意力模型和参数。注意力机制的加权融合可以使用numpy库中的矩阵乘法或加法等运算实现。
import cv2 import mediapipe as mp import time class FaceDetector(): def __init__(self, minDetectionCon=0.5): self.minDetectionCon = minDetectionCon self.mpFaceDetection = mp.solutions.face_detection self.mpDraw = mp.solutions.drawing_utils self.faceDetection = self.mpFaceDetection.FaceDetection(self.minDetectionCon) def findFaces(self, img, draw=True): imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) self.results = self.faceDetection.process(imgRGB) # print(self.results) bboxs = [] if self.results.detections: for id, detection in enumerate(self.results.detections): bboxC = detection.location_data.relative_bounding_box ih, iw, ic = img.shape bbox = int(bboxC.xmin * iw), int(bboxC.ymin * ih), \ int(bboxC.width * iw), int(bboxC.height * ih) bboxs.append([id, bbox, detection.score]) if draw: img = self.fancyDraw(img,bbox) cv2.putText(img, f'{int(detection.score[0] * 100)}%', (bbox[0], bbox[1] - 20), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 255), 2) return img, bboxs def fancyDraw(self, img, bbox, l=30, t=5, rt= 1): x, y, w, h = bbox x1, y1 = x + w, y + h cv2.rectangle(img, bbox, (255, 0, 255), rt) # Top Left x,y cv2.line(img, (x, y), (x + l, y), (255, 0, 255), t) cv2.line(img, (x, y), (x, y+l), (255, 0, 255), t) # Top Right x1,y cv2.line(img, (x1, y), (x1 - l, y), (255, 0, 255), t) cv2.line(img, (x1, y), (x1, y+l), (255, 0, 255), t) # Bottom Left x,y1 cv2.line(img, (x, y1), (x + l, y1), (255, 0, 255), t) cv2.line(img, (x, y1), (x, y1 - l), (255, 0, 255), t) # Bottom Right x1,y1 cv2.line(img, (x1, y1), (x1 - l, y1), (255, 0, 255), t) cv2.line(img, (x1, y1), (x1, y1 - l), (255, 0, 255), t) return img def main(): cap = cv2.VideoCapture("Videos/6.mp4") pTime = 0 detector = FaceDetector() while True: success, img = cap.read() img, bboxs = detector.findFaces(img) print(bboxs) cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTime cv2.putText(img, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2) cv2.imshow("Image", img) cv2.waitKey(1) if __name__ == "__main__": main() 给以上代码进行解析讲解,并告诉我代码的亮点和难点
这段代码实现了使用 Mediapipe 库进行人脸检测,并在检测到人脸时在人脸周围绘制矩形框和关键点,并显示帧率。以下是代码的解析讲解:
1. 导入需要的库:
```python
import cv2
import mediapipe as mp
import time
```
2. 创建人脸检测器的类 FaceDetector,并初始化类中的参数:
```python
class FaceDetector():
def __init__(self, minDetectionCon=0.5):
self.minDetectionCon = minDetectionCon
self.mpFaceDetection = mp.solutions.face_detection
self.mpDraw = mp.solutions.drawing_utils
self.faceDetection = self.mpFaceDetection.FaceDetection(self.minDetectionCon)
```
其中,minDetectionCon 表示人脸检测的置信度阈值,mpFaceDetection 和 mpDraw 是 Mediapipe 库中的人脸检测和绘图工具类,faceDetection 是人脸检测器对象。
3. 在 findFaces 方法中,调用人脸检测器对象的 process 方法进行人脸检测,并将检测结果中的人脸位置信息和置信度提取出来:
```python
def findFaces(self, img, draw=True):
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.results = self.faceDetection.process(imgRGB)
bboxs = []
if self.results.detections:
for id, detection in enumerate(self.results.detections):
bboxC = detection.location_data.relative_bounding_box
ih, iw, ic = img.shape
bbox = int(bboxC.xmin * iw), int(bboxC.ymin * ih), \
int(bboxC.width * iw), int(bboxC.height * ih)
bboxs.append([id, bbox, detection.score])
```
其中,bbox 表示人脸的矩形框的左上角坐标和宽高,bboxs 是一个列表,存储每个检测到的人脸的信息,包括人脸 ID、矩形框和置信度。
4. 在 fancyDraw 方法中,根据人脸位置信息和关键点绘制矩形框和关键点:
```python
def fancyDraw(self, img, bbox, l=30, t=5, rt= 1):
x, y, w, h = bbox
x1, y1 = x + w, y + h
cv2.rectangle(img, bbox, (255, 0, 255), rt)
# Top Left x,y
cv2.line(img, (x, y), (x + l, y), (255, 0, 255), t)
cv2.line(img, (x, y), (x, y+l), (255, 0, 255), t)
# Top Right x1,y
cv2.line(img, (x1, y), (x1 - l, y), (255, 0, 255), t)
cv2.line(img, (x1, y), (x1, y+l), (255, 0, 255), t)
# Bottom Left x,y1
cv2.line(img, (x, y1), (x + l, y1), (255, 0, 255), t)
cv2.line(img, (x, y1), (x, y1 - l), (255, 0, 255), t)
# Bottom Right x1,y1
cv2.line(img, (x1, y1), (x1 - l, y1), (255, 0, 255), t)
cv2.line(img, (x1, y1), (x1, y1 - l), (255, 0, 255), t)
return img
```
5. 在 main 函数中,读取视频流,并调用人脸检测器的 findFaces 方法进行人脸检测和绘制:
```python
def main():
cap = cv2.VideoCapture("Videos/6.mp4")
pTime = 0
detector = FaceDetector()
while True:
success, img = cap.read()
img, bboxs = detector.findFaces(img)
print(bboxs)
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(img, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2)
cv2.imshow("Image", img)
cv2.waitKey(1)
```
6. 代码亮点:
- 使用 Mediapipe 库中的人脸检测器和绘图工具类,简化了人脸检测和绘制的实现过程;
- 在 fancyDraw 方法中,根据人脸位置信息和关键点绘制矩形框和关键点,使得人脸检测结果更加直观。
7. 代码难点:
- 对于初学者来说,理解 Mediapipe 库中的人脸检测器和绘图工具类的使用方法可能比较困难;
- 在 fancyDraw 方法中,需要根据人脸位置信息和关键点计算出矩形框的位置和大小,并绘制出矩形框和关键点,需要一定的数学基础和编程经验。