Yolo(You only look once)

import cv2
import numpy as np
import matplotlib.pyplot as plt

print(cv2.__version__)

BLOBSIZE = 416
CONFIDENCE = 0.4

# https://pjreddie.com/darknet/yolo/ 320 다운
net = cv2.dnn.readNet("yolo/yolov3.weights", "yolo/yolov3.cfg")
classes = []
# 80개의 이름 클래스 [person, bicycle...]
with open("yolo/coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

img = cv2.imread("yolo/myImage.jpg")
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
blob = cv2.dnn.blobFromImage(img, 0.00392, (BLOBSIZE, BLOBSIZE), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

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 > CONFIDENCE:# 신뢰도 체크
            # 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, 0.1, 0.4) # 같은물체에 대한 박스 제거

font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[i]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y - 10), font, 1, color, 2)
        print(label, confidences[i], x, y, w, h)

cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()