我正在开发一个程序，使用YOLO从视频中检测鱼。我制作了一个可以在YOLO v3的Google Colab命令上正常运行的数据集，但是当我在计算机上将其更改为Python代码时，它只能检测到一条鱼。
我尝试更改置信度，并在其他计算机上尝试了该代码，但没有任何效果，它只能按预期使用Google Colab。
import cv2
import numpy as np
# Load Yolo
net = cv2.dnn.readNet("yolov3-tiny-obj_last.weights", "yolov3-tiny-obj.cfg")
classes = []
with open("obj.name.txt", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# Loading image
img = cv2.imread("fish.jpg")
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
print(class_id)
confidence = scores[class_id]
if confidence > 0.1:
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.4)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[0]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y + 30), font, 3, color, 3)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
这是我目前得到的： https://cdn.discordapp.com/attachments/607646929307828237/625020531015548928/unknown.png
我想念的是什么，你能告诉我吗？
