Anaconda ile OpenCV kurulumu
Öncelikle conda ortamını kurmamız gerekiyor.
conda create --name opencv python=3.5
Ardından “opencv” isimli bu ortamı aktif ediyoruz.
source activate opencv
OpenCV kurulum komutunu girebiliriz.
conda install -c conda-forge opencv
OpenCV kurulumu için aşağıdaki komutta kullanılabilir.
conda install -c conda-forge/label/broken opencv
Eş zamanlı tespit kodu
Aşağıdaki kodu
python yolo_opencv.py -c /path/to/yolov3-tiny.cfg -w /path/to/yolov3-tiny_finally.weights -cl /path/to/objects.names
komutu ile çalıştırarak tespit sisteminizi başlatabilirsiniz. ** yukarıdaki komutta dosya yollarını düzenlemeyi unutmayın.
import cv2
import argparse
import numpy as np
ap = argparse.ArgumentParser()
ap.add_argument('-c', '--config',
help = 'path to yolo config file', default='/path/to/yolov3-tiny.cfg')
ap.add_argument('-w', '--weights',
help = 'path to yolo pre-trained weights', default='/path/to/yolov3-tiny_finally.weights')
ap.add_argument('-cl', '--classes',
help = 'path to text file containing class names',default='/path/to/objects.names')
args = ap.parse_args()
# Get names of output layers, output for YOLOv3 is ['yolo_16', 'yolo_23']
def getOutputsNames(net):
layersNames = net.getLayerNames()
return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# Darw a rectangle surrounding the object and its class name
def draw_pred(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
label = str(classes[class_id])
color = COLORS[class_id]
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# Define a window to show the cam stream on it
window_title= "Rubiks Detector"
cv2.namedWindow(window_title, cv2.WINDOW_NORMAL)
# Load names classes
classes = None
with open(args.classes, 'r') as f:
classes = [line.strip() for line in f.readlines()]
print(classes)
#Generate color for each class randomly
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
# Define network from configuration file and load the weights from the given weights file
net = cv2.dnn.readNet(args.weights,args.config)
# Define video capture for default cam
cap = cv2.VideoCapture(0)
while cv2.waitKey(1) < 0:
hasframe, image = cap.read()
#image=cv2.resize(image, (620, 480))
blob = cv2.dnn.blobFromImage(image, 1.0/255.0, (416,416), [0,0,0], True, crop=False)
Width = image.shape[1]
Height = image.shape[0]
net.setInput(blob)
outs = net.forward(getOutputsNames(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4
#print(len(outs))
# In case of tiny YOLOv3 we have 2 output(outs) from 2 different scales [3 bounding box per each scale]
# For normal normal YOLOv3 we have 3 output(outs) from 3 different scales [3 bounding box per each scale]
# For tiny YOLOv3, the first output will be 507x6 = 13x13x18
# 18=3*(4+1+1) 4 boundingbox offsets, 1 objectness prediction, and 1 class score.
# and the second output will be = 2028x6=26x26x18 (18=3*6)
for out in outs:
#print(out.shape)
for detection in out:
#each detection has the form like this [center_x center_y width height obj_score class_1_score class_2_score ..]
scores = detection[5:]#classes scores starts from index 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 = center_x - w / 2
y = center_y - h / 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
# apply non-maximum suppression algorithm on the bounding boxes
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
for i in indices:
i = i[0]
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_pred(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
cv2.putText(image, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))
cv2.imshow(window_title, image)
Kaynak :