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import gradio as gr
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
import datetime
import time
from skimage import io, color
from PIL import Image
from io import BytesIO
from skimage.io import imread, imsave
import imutils
# Location of files on your system
PATH_TO_FILES = '/home/ekene/Documents/working/ChoiceData/imglb2017/'
# Start by importing modules
from glob import glob
import cv2
import datetime
import sys
import os
import string
import warnings
from os.path import join
import matplotlib.pyplot as plt
def main(argv):
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S%f")
print("Initialising...")
if len(argv) == 2:
filename = argv[1]
full_path = PATH_TO_FILES + filename
img = imread(full_path)
scale_image = 2 # downscale the image
img = cv2.resize(img, (scale_image, scale_image))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#otsu = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#img = cv2.cvtColor(otsu, cv2.COLOR_GRAY2BGR)
# Apply histogram equalization
h, w = gray.shape[:2]
cv_he = cv2.equalizeHist(gray)
#cv2.imshow('HE', cv_he)
_, contours, hierarchy = cv2.findContours(cv_he, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# sort contours by contour area
# keep only contours that are larger than 10% of the image area
# (here we assume the largest contour will be around the "head")
filtered_contours = [c for c in contours if cv2.contourArea(c) > 1000]
#calculate distance between each contour and sort contours by distance
# print(filtered_contours, contours)
closest_contours = sorted(filtered_contours, key = cv2.contourArea, reverse=True)
closest_contours = closest_contours[:4]
# selected_contours = closest_contours
# sort by smallest contour area
#filtered_contours = sorted(filtered_contours, key = cv2.contourArea, reverse=True)
# used for testing
if len(filtered_contours) > 1:
return 0
cv2.drawContours(gray, [filtered_contours[0]], -1, (255,255,255), 2)
cv2.drawContours(gray, filtered_contours, -1, (255,255,255), 2)
cv2.imshow("search by smallest area", gray)
cv2.waitKey(0)
return 0
# adding to show a function image
def show_image(frame):
plt.subplot(1,2,1)
plt.title('Original Image')
plt.imshow(frame, cmap='gray')
plt.subplot(1,2,2)
plt.title('Image with contours drawn')
plt.imshow(frame, cmap='gray')
plt.show()
return 0
def image_face_detection(path_to_image):
# Initialise the trackbar
cv2.namedWindow('Frame')
cv2.namedWindow('Threshold')
current_image = cv2.imread(path_to_image)
frame = current_image
# threshold_trackbar = cv2.createTrackbar('Threshold', 'Frame', 51, 255, nothing)
# cv2.createTrackbar('Threshold', 'Frame', 100, 255, nothing)
# cv2.createTrackbar('Threshold', 'Frame', 130, 255, nothing)
# cv2.createTrackbar('Threshold', 'Frame', 150, 255, nothing)
# cv2.createTrackbar('Threshold', 'Frame', 90, 255, nothing)
# cv2.createTrackbar('Threshold', 'Frame', 180, 255, nothing)
#cv2.namedWindow("Frame")
#cv2.createTrackbar('Threshold', "Frame", 50, 255, nothing)
# Initialize the first frame, resize the window, start the timer
# first_time = True
first_time = True
# start_clock = time.time()
# main_loop()
# print('Run time ', time.time()-start_clock)
start_clock = time.time()
# Capture keyboard events
key = cv2.waitKey(1) & 0xFF
# Press space to pause or unpause the video |