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YOLOv3.py
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YOLOv3.py
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#import libraries
import cv2
import numpy as np
obj_file = 'obj.names'
obj_classes = []
net_config = "cfg/yolov3_training.cfg"
net_weights = "cfg/yolov3_my_training_last_1100.weights"
blob_size = 320
confidence_threshold = 0.5
nms_threshold = 0.3
with open (obj_file, 'rt') as f :
obj_classes = f.read().rstrip('\n').split('\n')
net = cv2.dnn.readNetFromDarknet(net_config, net_weights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
def findObjects(output, img):
img_h, img_w, img_c = img.shape
bboxes = []
class_ids = []
confidences = []
for member in output :
for detect_vector in member :
scores = detect_vector[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > confidence_threshold :
w, h = int(detect_vector[2] * img_w) , int(detect_vector[3] * img_h)
x, y = int((detect_vector[2] * img_w) - w/2) , int((detect_vector[3] * img_h)- h/2)
bboxes.append([x,y,w,h])
class_ids.append(class_id)
confidences.append(float(confidence))
indices = cv2.dnn.NMSBoxes(bboxes, confidences, confidence_threshold, nms_threshold)
for i in indices :
i = i[0]
bbox = bboxes[i]
x,y,w,h = bbox[0], bbox[1], bbox[2], bbox[3]
cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
cv2.putText(img, f'{obj_classes[class_ids[i]].upper()} {int(confidences[i]*100)}%',
(x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
#using camera
cap = cv2.VideoCapture(0+cv2.CAP_DSHOW)
while (cap.isOpened()) :
success, frame = cap.read()
blob = cv2.dnn.blobFromImage(frame, scalefactor = 1/255, size = (blob_size,blob_size), mean = (0,0,0), swapRB = True, crop = False)
net.setInput(blob)
out_names = net.getUnconnectedOutLayersNames()
output = net.forward(out_names)
findObjects(output, frame)
cv2.imshow('CAMERA', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()