我试图填充这个矩形，但是即使将代码更改为（将厚度更改为-10），也没有效果。我感觉全局变量与此有关。
以下是我附加的代码。
import cv2
import os
import numpy as np
from .utils import download_file
initialize = True
net = None
dest_dir = os.path.expanduser('~') + os.path.sep + '.cvlib' + os.path.sep + 'object_detection' + os.path.sep + 'yolo' + os.path.sep + 'yolov3'
classes = None
COLORS = np.random.uniform(0, 255, size=(80, 3))
def draw_bbox(img, bbox, labels, confidence, colors=None, write_conf=False):
global COLORS
global classes
if classes is None:
classes = populate_class_labels()
for i, label in enumerate(labels):
if colors is None:
color = COLORS[classes.index(label)]
else:
color = colors[classes.index(label)]
if write_conf:
label += ' ' + str(format(confidence[i] * 100, '.2f')) + '%'
cv2.rectangle(img, (bbox[i][0],bbox[i][1]), (bbox[i][2],bbox[i][3]), color,-1)
cv2.putText(img, label, (bbox[i][0],bbox[i][1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
return img
def detect_common_objects(image):
Height, Width = image.shape[:2]
scale = 0.00392
global classes
global dest_dir
config_file_name = 'yolov3.cfg'
config_file_abs_path = dest_dir + os.path.sep + config_file_name
weights_file_name = 'yolov3.weights'
weights_file_abs_path = dest_dir + os.path.sep + weights_file_name
url = 'https://github.com/arunponnusamy/object-detection-opencv/raw/master/yolov3.cfg'
if not os.path.exists(config_file_abs_path):
download_file(url=url, file_name=config_file_name, dest_dir=dest_dir)
url = 'https://pjreddie.com/media/files/yolov3.weights'
if not os.path.exists(weights_file_abs_path):
download_file(url=url, file_name=weights_file_name, dest_dir=dest_dir)
global initialize
global net
if initialize:
classes = populate_class_labels()
net = cv2.dnn.readNet(weights_file_abs_path, config_file_abs_path)
initialize = False
blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)
outs = net.forward(get_output_layers(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 and class_id=='person':
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])
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
bbox = []
label = []
conf = []
for i in indices:
i = i[0]
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
if str(classes[class_ids[i]])=='person':
bbox.append([round(x), round(y), round(x+w), round(y+h)])
label.append(str(classes[class_ids[i]]))
conf.append(confidences[i])
return bbox, label, conf