英文:
API Integration: IndexError: invalid index to scalar variable
问题
以下是您提供的代码的中文翻译部分：
我正在尝试使用FastAPI集成我的Yolov4 darknet自定义训练模型，但控制台上出现了IndexError: scalar变量的无效索引，并且在FastAPI本地服务器上出现了内部服务器错误。以下是我的用于运行API的代码：
```python
import cv2
import numpy as np
import tensorflow as tf
# 加载 YOLO v4 模型
model = cv2.dnn.readNetFromDarknet("yolov4_test.cfg", "yolov4_train_final.weights")
from fastapi import FastAPI, File, UploadFile
from typing import List, Tuple
app = FastAPI()
@app.post("/detect_objects")
async def detect_objects(image: UploadFile = File(...)) -> List[Tuple[str, Tuple[int, int, int, int]]]:
# 读取图像文件
image_bytes = await image.read()
nparr = np.frombuffer(image_bytes, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# 在图像上运行 YOLO v4 模型
blob = cv2.dnn.blobFromImage(img, 1/255.0, (608, 608), swapRB=True, crop=False)
model.setInput(blob)
layer_names = model.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
outputs = model.forward(output_layers)
# 提取边界框和类别标签
boxes = []
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * img.shape[1])
center_y = int(detection[1] * img.shape[0])
width = int(detection[2] * img.shape[1])
height = int(detection[3] * img.shape[0])
left = int(center_x - width / 2)
top = int(center_y - height / 2)
boxes.append((class_id, (left, top, width, height)))
# 将类别ID映射到类别标签
classes = ["枪支"]
results = []
for box in boxes:
class_label = classes[box[0]]
bbox = box[1]
results.append((class_label, bbox))
return results
控制台中的错误信息如下：
Traceback (most recent call last):
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\uvicorn\protocols\http\h11_impl.py", line 428, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\uvicorn\middleware\proxy_headers.py", line 78, in __call__
return await self.app(scope, receive, send)
# 其他错误信息...
IndexError: scalar变量的无效索引。
您正在尝试获得边界框和标签，并将它们作为服务器响应返回。
英文:
I am trying to integrate my Yolov4 darknet customed trained model using fast api but am getting an IndexError: invalid index to scalar variable on console and Internal Server Error on Fastapi local server. Following is my code to run the API:
import cv2
import numpy as np
import tensorflow as tf
# Load YOLO v4 model
model = cv2.dnn.readNetFromDarknet("yolov4_test.cfg", "yolov4_train_final.weights")
from fastapi import FastAPI, File, UploadFile
from typing import List, Tuple
app = FastAPI()
@app.post("/detect_objects")
async def detect_objects(image: UploadFile = File(...)) -> List[Tuple[str, Tuple[int, int, int, int]]]:
# Read image file
image_bytes = await image.read()
nparr = np.frombuffer(image_bytes, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# Run YOLO v4 model on image
blob = cv2.dnn.blobFromImage(img, 1/255.0, (608, 608), swapRB=True, crop=False)
model.setInput(blob)
layer_names = model.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
outputs = model.forward(output_layers)
# Extract bounding boxes and class labels
boxes = []
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * img.shape[1])
center_y = int(detection[1] * img.shape[0])
width = int(detection[2] * img.shape[1])
height = int(detection[3] * img.shape[0])
left = int(center_x - width / 2)
top = int(center_y - height / 2)
boxes.append((class_id, (left, top, width, height)))
# Map class IDs to class labels
classes = ["Gun"]
results = []
for box in boxes:
class_label = classes[box[0]]
bbox = box[1]
results.append((class_label, bbox))
return results
Following is the error i am getting in console:
Traceback (most recent call last):
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\uvicorn\protocols\http\h11_impl.py", line 428, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\uvicorn\middleware\proxy_headers.py", line 78, in __call__
return await self.app(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\fastapi\applications.py", line 276, in __call__
await super().__call__(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\applications.py", line 122, in __call__
await self.middleware_stack(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\middleware\errors.py", line 184, in __call__
raise exc
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\middleware\errors.py", line 162, in __call__
await self.app(scope, receive, _send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\middleware\exceptions.py", line 79, in __call__
raise exc
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\middleware\exceptions.py", line 68, in __call__
await self.app(scope, receive, sender)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 21, in __call__
raise e
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 18, in __call__
await self.app(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\routing.py", line 718, in __call__
await route.handle(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\routing.py", line 276, in handle
await self.app(scope, receive, send)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\starlette\routing.py", line 66, in app
response = await func(request)
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\fastapi\routing.py", line 237, in app
raw_response = await run_endpoint_function(
File "c:\users\raafeh\desktop\fyp\envfast\lib\site-packages\fastapi\routing.py", line 163, in run_endpoint_function
return await dependant.call(**values)
File "C:\Users\Raafeh\Desktop\FYP\main.py", line 27, in detect_objects
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
File "C:\Users\Raafeh\Desktop\FYP\main.py", line 27, in <listcomp>
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
IndexError: invalid index to scalar variable.
I am trying get the bounding boxes and Label out as the server response
答案1
得分: 0
基于快速搜索结果，这与FastAPI无关，而是由于尝试索引NumPy标量，例如整数或浮点数所致。
快速查看代码会建议以下行中的 i 是一个整数，这导致了错误：
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
也许用 layer_names[i - 1] 替换 layer_names[i[0] - 1] 可能会解决这个问题。
英文:
Based on a quick search, this has nothing to do with FastAPI but rather is caused by trying to index a NumPy scalar, an integer or float for example.
A quick look at the code would suggest that the i on the following line is an integer, and that causes the error:
output_layers = [layer_names[i[0] - 1] for i in model.getUnconnectedOutLayers()]
Maybe replacing layer_names[i[0] - 1] with layer_names[i - 1] might solve this.
答案2
得分: 0
layer_names[i[0] - 1] 替换为 layer_names[i - 1]，但接着我遇到了另一个错误:
> cv2.error: 来自OpenCV代码的未知C++异常
这是我使用的OpenCV版本（4.7）引起的问题，我将其更改为4.6，API代码就正常工作了。
英文:
So my problem was solved by replacing layer_names[i[0] - 1] with layer_names[i - 1] but then I faced another error of
> cv2.error: Unknown C++ exception from OpenCV code
This was an issue with the OpenCV version I used which was 4.7, I changed it to 4.6 and volaa! My api code was working .
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