I couldn't find a solution to use YOLOv3 for single-class. I want to detect just for motorbikes. I edited the coco.names just for motorbikes, and edited the filters, classes in cfg file. But whenever i run my code it errors as
line 48, in <module>
for i in indexes.flatten():
AttributeError: 'tuple' object has no attribute 'flatten'". Here is my code.
import cv2
import numpy as np
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
classes = []
with open('coco.names', 'r') as f:
classes = f.read().splitlines()
cap = cv2.VideoCapture('test.mp4')
#img = cv2.imread('image.jpg')
while True:
_, img = cap.read()
height, width, _ = img.shape
blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), (0,0,0), swapRB=True, crop=False)
net.setInput(blob)
output_layers_names = net.getUnconnectedOutLayersNames()
layerOutputs = net.forward(output_layers_names)
boxes = []
confidences = []
class_ids = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0]*width)
center_y = int(detection[1]*height)
w = int(detection[2]*width)
h = int(detection[3]*height)
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, 0.5, 0.4)
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(boxes), 3))
for i in indexes.flatten():
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = str(round(confidences[i], 2))
color = colors[i]
cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
cv2.putText(img, label + " " + confidence, (x, y+20), font, 2, (0, 0, 0), 2)
cv2.imshow('Image', img)
key = cv2.waitKey(1)
if key==27:
break
cap.release()
cv2.destroyAllWindows()
