n[1], line 23 21 frame = cv2.resize(frame, (width, height)) 22 #行人检测 ---> 23 (rects, weights) = hog.detectMultiScale(frame, winStride=(4.4), padding=(8, 8), scale=1.05) 24 #遍历检测结果 25 for i, (x, y, w, h) in enumerate(rects): 26 #去除重复检测 error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'detectMultiScale' > Overload resolution failed: > - Can't parse 'winStride'. Input argument doesn't provide sequence protocol > - Can't parse 'winStride'. Input argument doesn't provide sequence protocol
时间: 2024-04-26 22:23:44 浏览: 20
根据报错信息可以看出问题是 "winStride" 参数的输入类型不正确,导致了 "detectMultiScale" 函数的调用失败。根据 OpenCV 官方文档,"winStride" 参数需要传入一个元组类型的值,表示 HOG 检测窗口在水平方向和垂直方向上的步长。建议您将代码中的 "winStride" 参数的值改为元组类型的值,例如:
(rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05)
这样就可以避免该报错的出现。同时,建议您也检查一下代码中其他参数是否有输入错误的情况。
相关问题
为我解释每一行代码,增加注释 def video_recognize(v_path, s_path): cap = cv2.VideoCapture(v_path) fps = cap.get(cv2.CAP_PROP_FPS) # 帧率 w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # 宽 h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 高 fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') # 指定视频编码方式 videoWriter = cv2.VideoWriter(s_path, fourcc, fps, (w, h)) # 创建视频写对象 frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # 视频总帧数 if v_path == 0: while 1: # Find haar cascade to draw bounding box around face ret, frame = cap.read() facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = facecasc.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10), (255, 0, 0), 2) roi_gray = gray[y:y + h, x:x + w] cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0) prediction = model.predict(cropped_img) maxindex = int(np.argmax(prediction)) cv2.putText(frame, emotion_dict[maxindex], (x + 20, y - 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA) # show the output frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break
```
def video_recognize(v_path, s_path):
# 打开视频文件
cap = cv2.VideoCapture(v_path)
# 获取视频的帧率、宽度和高度
fps = cap.get(cv2.CAP_PROP_FPS)
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# 指定视频编码方式
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
# 创建视频写对象
videoWriter = cv2.VideoWriter(s_path, fourcc, fps, (w, h))
# 获取视频的总帧数
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# 如果输入的是摄像头设备号
if v_path == 0:
while 1:
# 读取一帧图像
ret, frame = cap.read()
# 使用Haar级联分类器检测人脸
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
# 针对每张人脸,进行情绪识别并在图像上标出人脸和情绪
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
prediction = model.predict(cropped_img)
maxindex = int(np.argmax(prediction))
cv2.putText(frame, emotion_dict[maxindex], (x + 20, y - 60), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2, cv2.LINE_AA)
# 显示图像
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# 如果按下键盘的q键,则退出程序
if key == ord("q"):
break
```
这段代码实现了从视频文件中读取帧图像,识别出其中的人脸并进行情绪分析,最后将分析结果写入新的视频文件中。
其中,`v_path`是输入视频文件的路径,`s_path`是输出视频文件的路径。`cap`是视频捕获对象,`fps`、`w`、`h`和`frame_count`分别表示帧率、宽度、高度和总帧数。`fourcc`是视频编码方式,`videoWriter`是视频写对象。
如果输入的是摄像头设备号,则进入循环,不断读取一帧图像并使用Haar级联分类器检测人脸。对于每张人脸,先在图像上标出人脸的位置,再对人脸进行情绪分析并在图像上标出情绪。最后将处理后的图像显示出来,如果按下键盘的q键,则退出程序。
帮我继续写代码,实现一条线双线计数的功能。 while True: # 读取每帧图片 _, im = capture.read() if im is None: break # 缩小尺寸 im = cv2.resize(im, (width // 2, height // 2)) list_bboxs = [] # 更新跟踪器 # 跟踪器传入检测的模型和处理之后的图片 output_image_frame, list_bboxs = objtracker.update(detector, im) # 输出图片 output_image_frame = cv2.add(output_image_frame, color_polygons_image) if len(list_bboxs) > 0:
Certainly! Here's a continuation of the code to implement the line counting functionality:
```python
# Iterate through each bounding box detected
for bbox in list_bboxs:
x, y, w, h = bbox
# Check if the bounding box intersects with the blue line
if y + h/2 > h/2 and y + h/2 < h/2 + 20:
# Increment blue line counter
blue_line_count += 1
# Check if the bounding box intersects with the yellow line
if y + h/2 > h/2 + 70 and y + h/2 < h/2 + 90:
# Increment yellow line counter
yellow_line_count += 1
# Display the counts on the output image
cv2.putText(output_image_frame, f"Blue Line Count: {blue_line_count}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.putText(output_image_frame, f"Yellow Line Count: {yellow_line_count}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# Display the output image
cv2.imshow("Output", output_image_frame)
# Break the loop if "q" is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture and destroy all windows
capture.release()
cv2.destroyAllWindows()
```
In this code, we iterate through each bounding box detected (`list_bboxs`) and check if it intersects with the blue or yellow lines. If a bounding box intersects with the blue line, we increment the `blue_line_count` variable. If a bounding box intersects with the yellow line, we increment the `yellow_line_count` variable.
We then display the counts on the output image using `cv2.putText()` and show the output image using `cv2.imshow()`. The loop continues until the "q" key is pressed.
Finally, we release the video capture and destroy all windows.