title: OpenCV Rectangle Filled
body: <p><a href="https://i.stack.imgur.com/5GLoi.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/5GLoi.jpg" alt="enter image description here"></a>I am trying to fill the rectangle but even after changing the code(chaning thickness to -10) there is no effect. I feel that the global has something to do with this. </p>
  
  <p>I have attached the code below. </p>
  
  <pre><code>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 &gt; 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
  </code></pre>
  
  <p>The entire code is the above. It is an object detection program using Yolo and opencv. I have also added few lines in the last line to enable only the person class but it seems to detect all classes. I have also tried to modify the thickness of the rectangles but changing the values had no effect.</p>