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object-detection.py
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object-detection.py
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import cv2
import time
CONFIDENCE_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("Test_Video/test_4.mp4")
cap = cv2.VideoCapture(0)
net = cv2.dnn.readNet("yolov4-tiny_best.weights", "yolov4-tiny.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:
_, frame = cap.read()
if not _:
exit()
start = time.time()
classes, scores, boxes = model.detect(frame, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
end = time.time()
start_drawing = time.time()
for (classid, score, box) in zip(classes, scores, boxes):
color = COLORS[int(classid) % len(COLORS)]
label = "%s : %f" % (class_names[classid], score)
cv2.rectangle(frame, box, color, 2)
cv2.putText(frame, label, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
end_drawing = time.time()
fps_label = "FPS: %.2f (excluding drawing time of %.2fms)" % (
1 / (end - start), (end_drawing - start_drawing) * 1000)
cv2.putText(frame, fps_label, (0, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
cv2.imshow("detections", frame)