YOLOv4 inference using OpenCV-DNN-CUDA on Linux
Run YOLOv4 inference using the OpenCV-DNN-CUDA module on Linux.
In the previous blog “Setup OpenCV-DNN module with CUDA backend support (For Linux)”, we built the OpenCV-DNN module with CUDA backend support on Linux. (I have Ubuntu 18.04). We can use this OpenCV module to run inference on object detection models. The OpenCV-DNN module built with CUDA backend support gives much faster inference as compared to the OpenCV without CUDA backend. All we need to do is add the following two lines to our code and infer the model using this.
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)The OpenCV-DNN module built from source with CUDA backend support gives much faster inference as compared to the OpenCV without CUDA backend.
Follow the steps below to run inference using the OpenCV-DNN module we have installed:
- First, open Anaconda prompt and activate the virtual environment that you used to install the OpenCV-DNN module.
- Next, Download the following Python opencv_dnn.py script and run it using Python. Note that this uses the yolov4 model weights and config file. Download them from here.
import cv2
import timeCONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]class_names = []
with open("classes.txt", "r") as f:
class_names = [cname.strip() for cname in f.readlines()]cap = cv2.VideoCapture("video.mp4")net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)model = cv2.dnn_DetectionModel(net)
model.setInputParams(size=(416, 416), scale=1/255, swapRB=True)while cv2.waitKey(1) < 1:
(grabbed, frame) = cap.read()
if not grabbed:
exit() start = time.time()
classes, scores, boxes = model.detect(frame, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
end = time.time() for (classid, score, box) in zip(classes, scores, boxes):
color = COLORS[int(classid) % len(COLORS)]
label = "%s : %f" % (class_names[classid[0]], score)
cv2.rectangle(frame, box, color, 2)
cv2.putText(frame, label, (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
fps = "FPS: %.2f " % (1 / (end - start))
cv2.putText(frame, fps, (0, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
cv2.imshow("output", frame)
You can also download the above Python code to test the OpenCV-DNN inference.
IMPORTANT: The OpenCV-DNN-CUDA module only supports inference. So although you will get much faster inference out of it, the training however will be the same as it was for the OpenCV we set up without CUDA backend support.
You can also use the OpenCV-DNN-CUDA module to run inference on models from various other frameworks like TensorFlow, PyTorch, etc.
