sabiipoks

import cv2
import numpy as np
from matplotlib import pyplot as plt
im = cv2.imread('input-image.jpg')
rows, cols = im.shape[:2]
# Create a Gaussian filter
kernel_x = cv2.getGaussianKernel(cols,200)
kernel_y = cv2.getGaussianKernel(rows,200)
kernel = kernel_y * kernel_x.T
filter = 255 * kernel / np.linalg.norm(kernel)

sabiipoks

import cv2
import matplotlib.pyplot as plt
im = cv2.imread('input-image.jpg')
edges = cv2.Canny(im,100,300)
plt.imshow(edges)
plt.show()

sabiipoks

import cv2
import matplotlib.pyplot as plt
im = cv2.imread('input-image.jpg')
dst = cv2.GaussianBlur(im,(5,5),cv2.BORDER_DEFAULT)
plt.imshow(dst)
plt.show()

sabiipoks

opencv-python
cvlib
matplotlib
tensorflow

sabiipoks

import cv2
import matplotlib.pyplot as plt
import cvlib as cv
from cvlib.object_detection import draw_bbox
im = cv2.imread('apple-256261_640.jpg')
bbox, label, conf = cv.detect_common_objects(im)
output_image = draw_bbox(im, bbox, label, conf)
plt.imshow(output_image)
plt.show()

sabiipoks

# This script has been tested using a Raspberry Pi Camera Modeule v1.3
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2 as cv
import math
import dnn
import numpy as np

detectum = dnn.FaceDetector()

sabiipoks

import dnn
import cv2 as cv
im = cv.imread('image.jpg')
_, bboxes = dnn.FaceDetector().process_frame(im, threshold=0.4) 
for i in bboxes:    
     cv.rectangle(im, (i[0], i[1]), (i[2], i[3]), (0, 255, 0), 3)
cv.imwrite('/image_output.jpg', im)

sabiipoks

from google.cloud import vision
import io
from PIL import Image, ImageDraw

client = vision.ImageAnnotatorClient()

image_path = 'image_4.jpg'
with io.open(image_path, 'rb') as image_file:
    content = image_file.read()

sabiipoks

import dnn
import cv2 as cv

im = cv.imread('image.jpg')
_, bboxes = dnn.FaceDetector().process_frame(im, threshold=0.4)

for i in bboxes:
    cv.rectangle(im, (i[0], i[1]), (i[2], i[3]), (0, 255, 0), 3)
cv.imwrite('/image_output.jpg', im)

sabiipoks

import boto3
from PIL import Image, ImageDraw

BUCKET = "<BUCKET_NAME>"
KEY = "<IMAGE_KEY>"
ACCESS_KEY = "<ACCESS_KEY>"
SECRET_KEY = "<SECRET_KEY>"
FEATURES_BLACKLIST = ("Landmarks", "Emotions", "Pose", "Quality", "BoundingBox", "Confidence")

def detect_faces(bucket, key, attributes=['ALL'], region="<REGION>"):