问题描述
我有一个带有 yolov4 的对象跟踪 python 代码，它在我的计算机上运行良好。 但是我的笔记本电脑上没有适合的 GPU，而且我的输出视频的 fps 很低。 我试图用 cuda 用 gpu 重建 opencv，但我的卡不支持。
colab 是我最后一次更改，我从昨晚开始一直在搜索它，但无论如何都没有进展。
你能告诉我一些如何使用 colab 做到这一点的方法吗？
这是我的代码：
import cv2 as cv
import numpy as np
import time
import math
class EuclideandistTracker:
def __init__(self):
# Store the center positions of the objects
self.center_points = {}
# Keep the count of the IDs
# each time a new object id detected,the count will increase by one
self.id_count = 0
def update(self,objects_rect):
# Objects Boxes and ids
objects_bbs_ids = []
# Get center point of new object
for rect in objects_rect:
x,y,w,h = rect
cx = (x + x + w) // 2
cy = (y + y + h) // 2
# Find out if that object was detected already
same_object_detected = False
for id,pt in self.center_points.items():
dist = math.hypot(cx - pt[0],cy - pt[1])
if dist < 25 or pt[1] > 350 :
self.center_points[id] = (cx,cy)
#print(self.center_points)
objects_bbs_ids.append([x,h,id])
same_object_detected = True
break
# New object is detected we assign the ID to that object
if same_object_detected is False:
self.center_points[self.id_count] = (cx,cy)
objects_bbs_ids.append([x,self.id_count])
self.id_count += 1
# Clean the dictionary by center points to remove IDS not used anymore
new_center_points = {}
for obj_bb_id in objects_bbs_ids:
_,_,object_id = obj_bb_id
center = self.center_points[object_id]
new_center_points[object_id] = center
# Update dictionary with IDs not used removed
self.center_points = new_center_points.copy()
return objects_bbs_ids
# Create tracker object
tracker = EuclideandistTracker()
# Load Yolo
net = cv.dnn.readNet("yolov4-tiny_best.weights","yolov4-tiny.cfg")
net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)
# print(cv.cuda.getCudaEnabledDeviceCount())
classes = []
with open("obj.names","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()]
# Loading video
cap = cv.VideoCapture("test2.mp4")
cap.set(3,1280) # set video widht
cap.set(4,720) # set video height
font = cv.FONT_HERShey_COMPLEX_SMALL
starting_time = time.time()
frame_id = 0
toplam = 0
print("\033[H\033[J")
while True:
# Get frame
_,frame = cap.read()
frame_id += 1
height,width,channels = frame.shape
# Detecting objects
blob = cv.dnn.blobFromImage(frame,0.00261,(416,416),(0,0),True,crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
result = []
Boxes = []
liste = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.7:
# 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,h])
Boxes_ids = tracker.update(Boxes)
eklenecekler = []
filtered =[]
for i in range(len(Boxes_ids)):
if Boxes_ids[i][4] not in eklenecekler:
eklenecekler.append(Boxes_ids[i][4])
filtered.append(Boxes_ids[i])
for Box_id in filtered:
x,id = Box_id
cv.rectangle(frame,(x,y+h-20),(x+30,y+h),255),-1)
cv.putText(frame,str(id+1),y + h),font,1,(255,255,1)
cv.rectangle(frame,y),(x + w,2)
# For fps
elapsed_time = time.time() - starting_time
fps = frame_id / elapsed_time
#total car count
try:
_,value = max(filtered,key=lambda item: item[4])
if value > toplam:
toplam = value
except:
pass
cv.line(frame,350),(1280,2)
#Some texts
cv.putText(frame,"FPS: " + str(round(fps,2)),(5,156),2)
cv.putText(frame,"NAZIM CORAKLI",(600,70),str(toplam+1),(1200,2,2)
#Final
cv.imshow("Awsome Car Counting",frame)
# Press 'ESC' for exiting video
k = cv.waitKey(1) & 0xff
if k == 27:
break
if k == ord('p'):
cv.waitKey(-1) #wait until any key is pressed
cap.release()
cv.destroyAllWindows()
解决方法
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