aravindpai

import pandas as pd

thresh=100
p1,p2,d=find_closest(dist,num,thresh)
df = pd.DataFrame({"p1":p1,"p2":p2,"dist":d})
df

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%%time
def find_closest(dist,num,thresh):
  p1=[]
  p2=[]
  d=[]
  for i in range(num):
    for j in range(i,num):
      if( (i!=j) & (dist[i][j]<=thresh)):
        p1.append(i)
        p2.append(j)

aravindpai

%%time
from scipy.spatial import distance
def compute_distance(midpoints,num):
  dist = np.zeros((num,num))
  for i in range(num):
    for j in range(i+1,num):
      if i!=j:
        dst = distance.euclidean(midpoints[i], midpoints[j])
        dist[i][j]=dst
  return dist

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#call the function
midpoints = [mid_point(img,person,i) for i in range(len(person))]

#visualize image
plt.figure(figsize=(20,10))
plt.imshow(img)

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#define a function which return the bottom center of every bbox
def mid_point(img,person,idx):
  #get the coordinates
  x1,y1,x2,y2 = person[idx]
  _ = cv2.rectangle(img, (x1, y1), (x2, y2), (0,0,255), 2)
  
  #compute bottom center of bbox
  x_mid = int((x1+x2)/2)
  y_mid = int(y2)
  mid   = (x_mid,y_mid)

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#compute center 
x_center = int((x1+x2)/2)
y_center = int(y2)

center = (x_center, y_center)

_ = cv2.circle(img, center, 5, (255, 0, 0), -1)
plt.figure(figsize=(20,10))
plt.imshow(img)

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img = cv2.imread('frames/30.png')
_ = cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 2)

plt.figure(figsize=(20,10))
plt.imshow(img)

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#identity only persons 
ind = np.where(classes==0)[0]

#identify bounding box of only persons
person=bbox[ind]

#total no. of persons
num= len(person)

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# Use `Visualizer` to draw the predictions on the image.
v = Visualizer(img[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(v.get_image()[:, :, ::-1])

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cfg = get_cfg()

# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_C4_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.9  # set threshold for this model

# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_C4_3x.yaml")
predictor = DefaultPredictor(cfg)