It should be enough to make it use CUDA and you should see a noticeable speedup. Check your Task Manager's Performance tab and see if your Nvidia GPU is being used. I don't know what's happening behind the scenes in my_function(), so I can't tell if there's something different in how you're using it.
This is the class that I usually copy around when I want a quick setup for the Yolo network
import cv2
import numpy as np
class Yolo:
def __init__(self, cfg, weights, names, conf_thresh, nms_thresh, use_cuda = False):
# save thresholds
self.ct = conf_thresh;
self.nmst = nms_thresh;
# create net
self.net = cv2.dnn.readNet(weights, cfg);
print("Finished: " + str(weights));
self.classes = [];
file = open(names, 'r');
for line in file:
self.classes.append(line.strip());
# use gpu + CUDA to speed up detections
if use_cuda:
self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA);
self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA);
# get output names
layer_names = self.net.getLayerNames();
self.output_layers = [layer_names[i[0]-1] for i in self.net.getUnconnectedOutLayers()];
# runs detection on the image and draws on it
def detect(self, img, target_id = None):
# get detection stuff
b, c, ids, idxs = self.get_detection_data(img, target_id);
# draw result
img = self.draw(img, b, c, ids, idxs);
return img, len(idxs);
# returns boxes, confidences, class_ids, and indexes (indices?)
def get_detection_data(self, img, target_id = None):
# get output
layer_outputs = self.get_inf(img);
# get dims
height, width = img.shape[:2];
# filter thresholds and target
b, c, ids, idxs = self.thresh(layer_outputs, width, height, target_id);
return b, c, ids, idxs;
# runs the network on an image
def get_inf(self, img):
# construct a blob
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416,416), swapRB=True, crop=False);
# get response
self.net.setInput(blob);
layer_outputs = self.net.forward(self.output_layers);
return layer_outputs;
# filters the layer output by conf, nms and id
def thresh(self, layer_outputs, width, height, target_id = None):
# some lists
boxes = [];
confidences = [];
class_ids = [];
# each layer outputs
for output in layer_outputs:
for detection in output:
# get id and confidence
scores = detection[5:];
class_id = np.argmax(scores);
confidence = scores[class_id];
# filter out low confidence
if confidence > self.ct:
# filter by target_id if set
if target_id is None or class_id == target_id:
# scale bounding box back to the image size
box = detection[0:4] * np.array([width, height, width, height]);
(cx, cy, w, h) = box.astype('int');
# grab the top-left corner of the box
tx = int(cx - (w / 2));
ty = int(cy - (h / 2));
# update lists
boxes.append([tx,ty,int(w),int(h)]);
confidences.append(float(confidence));
class_ids.append(class_id);
# apply NMS
idxs = cv2.dnn.NMSBoxes(boxes, confidences, self.ct, self.nmst);
return boxes, confidences, class_ids, idxs;
# draw detections on image
def draw(self, img, boxes, confidences, class_ids, idxs):
# check for zero
if len(idxs) > 0:
# loop over indices
for i in idxs.flatten():
# extract the bounding box coords
(x,y) = (boxes[i][0], boxes[i][1]);
(w,h) = (boxes[i][2], boxes[i][3]);
# draw a box
cv2.rectangle(img, (x,y), (x+w,y+h), (0,0,255), 2);
# draw text
text = "{}: {:.4}".format(self.classes[class_ids[i]], confidences[i]);
cv2.putText(img, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2);
return img;
You can use it like this
import cv2
import numpy as np
# this is the "yolo.py" file, I assume it's in the same folder as this program
from yolo import Yolo
# these are the filepaths of the yolo files
weights = "yolov3-tiny.weights";
config = "yolov3-tiny.cfg";
labels = "yolov3.txt";
# init yolo network
target_class_id = 79; # toothbrush
conf_thresh = 0.4; # less == more boxes (but more false positives)
nms_thresh = 0.4; # less == more boxes (but more overlap)
net = Yolo(config, weights, labels, conf_thresh, nms_thresh, use_cuda = True);
# open video capture
cap = cv2.VideoCapture(0); # probably laptop webcam
# loop
done = False;
while not done:
# get frame
ret, frame = cap.read();
if not ret:
done = cv2.waitKey(1) == ord('q');
continue;
# draw detection
# frame, _ = net.detect(frame, target_id=target_class_id); # use this to filter by a single class_id
frame, _ = net.detect(frame); # use this to not filter by class_id
# show
cv2.imshow("Marked", frame);
done = cv2.waitKey(1) == ord('q');
Edit: Oh, ok, lol. I found my install notes and the very first instruction is this:
- Follow PyImageSearch's Blog to install opencv with cuda support
The url is:
https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/
Not sure how helpful this will be, but I apparently thought the tutorial was clear enough that I didn't need any additional notes or corrections for this step.
