OpenCV Body Detection

haar.py

import time
import numpy as np
import cv2
from matplotlib import pyplot as plt

photos = [
    'C:\\Users\\jefer\\Pictures\\pessoas.jpg',
]

img = cv2.imread(photos[0],cv2.IMREAD_COLOR)
body_cascade = cv2.CascadeClassifier('haarcascade_fullbody.xml')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

bodies = body_cascade.detectMultiScale(gray, 1.1, 3)
    
for (x,y,w,h) in bodies:
    cv2.rectangle(img, (x,y), (x+w, y+h), (12,150,100), 2)
    
cv2.imshow('image',img)
cv2.waitKey(0) # If you don'tput this line,thenthe image windowis just a flash. If you put any number other than 0, the same happens.
cv2.destroyAllWindows()

yolo.py

import time
import numpy as np
import cv2
from matplotlib import pyplot as plt

photos = [
    'C:\\Users\\jefer\\Pictures\\pessoas.jpg',
]

model_name = 'yolov3-tiny'

net = cv2.dnn.readNetFromDarknet('{}.cfg.txt'.format(model_name), '{}.weights'.format(model_name))
labels = open('coco.names.txt').read().strip().split("\n")
colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

image = cv2.imread(photos[2])
(input_height, input_width) = image.shape[:2]

layer_names = net.getLayerNames()
layer_names = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)

start = time.time()
layerOutputs = net.forward(layer_names)
end = time.time()

print("detection took {:.6f} seconds".format(end - start))

boxes = []
confidences = []
classIDs = []
min_confidence = 0.5
threshold = 0.3

for output in layerOutputs:
    for detection in output:
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]
        if confidence < min_confidence:
            continue
        # scale the bounding box coordinates back relative to the
        # size of the image, keeping in mind that YOLO actually
        # returns the center (x, y)-coordinates of the bounding
        # box followed by the boxes' width and height
        box = detection[0:4] * np.array([input_width, input_height, input_width, input_height])
        (centerX, centerY, width, height) = box.astype("int")
        # use the center (x, y)-coordinates to derive the top and
        # and left corner of the bounding box
        x = int(centerX - (width / 2))
        y = int(centerY - (height / 2))
        # update our list of bounding box coordinates, confidences,
        # and class IDs
        boxes.append([x, y, int(width), int(height)])
        confidences.append(float(confidence))
        classIDs.append(classID)

idxs = cv2.dnn.NMSBoxes(boxes, confidences, min_confidence, threshold)

if len(idxs) > 0:
    for i in idxs.flatten():
        # extract the bounding box coordinates
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])
        # draw a bounding box rectangle and label on the image
        color = [int(c) for c in colors[classIDs[i]]]
        cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
        text = "{}: {:.4f}".format(labels[classIDs[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

cv2.imshow("Image", image)
cv2.waitKey(0)

https://pjreddie.com/darknet/yolo/