[英]How do i make detection only on spesific area
I'm working on a project trying to do object detection and text detection using both yolo and easyocr.我正在做一个项目,试图使用 yolo 和 easyocr 进行 object 检测和文本检测。 Since I'm a beginner and really new to computer vision, I would be glad if someone can help me.
由于我是初学者并且对计算机视觉真的很陌生,如果有人能帮助我,我会很高兴。
Here's the code:这是代码:
import cv2
import numpy as np
import easyocr
# Load Yolo
net = cv2.dnn.readNet('yolov4-tiny-custom_3000.weights', 'yolov4-tiny-custom.cfg')
classes = []
with open("obj.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
cap = cv2.VideoCapture('car1.mp4')
# Declare Ocr
cascade_src = 'haarcascade_russian_plate_number.xml'
cascade = cv2.CascadeClassifier(cascade_src)
reader = easyocr.Reader(['en'], gpu = False)
# Declare Ocr
while True:
_, frame = cap.read()
height, width, channels = frame.shape
#frame = cv2.resize(frame, (800, 600))
# Yolo Detection
# Detecting objects
blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
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)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
cv2.putText(frame, label, (x, y + 30), cv2.FONT_HERSHEY_PLAIN, 3, color, 3)
print("Jenis Mobil: " +label)
# Text Reader Using Ocr
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
plate = cascade.detectMultiScale(gray, 1.1, 5)
for x,y,w,h in plate:
wT,hT,cT = frame.shape
a,b = (int(0.02*wT),int(0.02*hT))
plate2 = frame[y+a:y+h-a,x+b:x+w-b,:]
cv2.rectangle(frame,(x,y),(x+w,y+h),(60,60,255),2)
cv2.rectangle(frame,(x-1,y-40),(x+w+1,y),(60,60,255),-1)
result = reader.readtext(plate2)
for detek in result:
top_left = (int(detek[0][0][0]), int(detek[0][0][1]))
bottom_right = (int(detek[0][2][0]), int(detek[0][2][1]))
text = detek[1]
cv2.putText(frame,text,(x,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,(255,255,255),2)
print("Nomor Kendaran: " + text)
# Text Reader Using Ocr
cv2.imshow("Detection", frame)
key = cv2.waitKey(1)
if key == 27:
break
cap.release()
cv2.destroyAllWindows()
I am trying to use ROI to detect the object but I am not able to do it.我正在尝试使用 ROI 来检测 object,但我做不到。 Any advice please?
有什么建议吗?
Crop the image before it is fed to the model在将图像馈送到 model 之前裁剪图像
while True:
_, frame = cap.read()
im_crop = im[y1:y2, x1:x2] # set x1,x2,y1,y2 based on your ROI
blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
this will speed up the inference time as well as there is less data to process by the model这将加快推理时间,并且 model 需要处理的数据更少
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