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DetectNail.py
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DetectNail.py
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import cv2
from matplotlib.pyplot import contour
import numpy as np
import matplotlib.pyplot as plt
from random import randint
import matplotlib.pyplot as plt
def DetectPositionMaxSkin(filename,x, y, w, h, lower, upper):
#y=y+50
Image = cv2.VideoCapture(filename)
#Image = cv2.VideoCapture('t8.mp4')
success, frame = Image.read()
while success :
success, frame = Image.read()
#cv2.imshow('Imagem Original', frame)
if success:
cropeedIMAGE = frame[y:y+h, x:x+w]
converted = cv2.cvtColor(cropeedIMAGE, cv2.COLOR_BGR2HSV)
#cv2.imshow('convertedHSV',converted)
skinMask = cv2.inRange(converted, lower, upper)
#cv2.imshow('skin',skinMask)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (12, 12))
skinMask = cv2.erode(skinMask, kernel, iterations=3)
skinMask = cv2.dilate(skinMask, kernel, iterations=3)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (11, 11), 5)
#cv2.imshow('skinMask',skinMask)
skin = cv2.bitwise_and(cropeedIMAGE, cropeedIMAGE, mask=skinMask)
#cv2.imshow('skin',skin)
########################################################
#lowerFinger =np.array([8, 15, 110], dtype="uint8")
#upperFinger = np.array([8, 15, 110], dtype="uint8")
hsv_img = cv2.cvtColor(skin, cv2.COLOR_BGR2HSV)
#hsv_img = cv2.inRange(hsv_img, lowerFinger, upperFinger)
#cv2.imshow('hsv_img', hsv_img)
# Extracting Saturation channel on which we will work
img_s = hsv_img[:, :, 1]
#img_s = skin[:, :, 1]
#cv2.imshow('img_s', img_s)
# smoothing before applying threshold
img_s_blur = cv2.GaussianBlur(img_s, (7,7), 2)
#img_s_blur = cv2.medianBlur(skin,5)
#cv2.imshow('img_s_blur', img_s_blur)
img_s_binary = cv2.threshold(img_s_blur, 200, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] # Thresholding to generate binary image (ROI detection)
#cv2.imshow('img_s_binary1', img_s_binary1)
# reduce some noise
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
img_s_binary = cv2.morphologyEx(img_s_binary, cv2.MORPH_OPEN, kernel, iterations=4)
#cv2.imshow('img_s_binary1', img_s_binary)
# ROI only image extraction & contrast enhancement, you can crop this region
#img_croped = cv2.bitwise_and(img_s, img_s_binary) * 10
#cv2.imshow('img_croped', img_croped)
# eliminate
kernel = np.ones((5, 5), np.float32)/25
processedImage = cv2.filter2D(img_s_binary, -1, kernel)
img_s_binary[processedImage > 250] = 0
#cv2.imshow('img_s_binary2', img_s_binary)
edges = cv2.threshold(img_s_binary, 100, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
#th3 = cv2.adaptiveThreshold(img_s_blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
#_,edges = cv2.threshold(img_croped, 160, 255, cv2.THRESH_BINARY_INV)
#cv2.imshow('edges', edges)
#https://docs.opencv.org/3.4/da/d0c/tutorial_bounding_rects_circles.html
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#print("Number of contours =" + str(len(contours)))
#print("Number of hierarchy =" + str(len(hierarchy)))
#print(np.argmax(hierarchy))
contours_poly = [None]*len(contours)
centers = [None]*len(contours)
radius = [None]*len(contours)
#area= [None]*len(contours)
#drawing = np.zeros((edges.shape[0], edges.shape[1], 3), dtype=np.uint8)
for i, c in enumerate(contours):
contours_poly[i] = cv2.approxPolyDP(c, 0.02, True)
#boundRect[i] = cv2.boundingRect(contours_poly[i])
centers[i], radius[i] = cv2.minEnclosingCircle(contours_poly[i])
#area[i] = cv2.contourArea(contours[i])
#print("area: %s" % area)
#if i>=6 and cv2.contourArea(contours[i]) >= 100:
if 5000 >= cv2.contourArea(contours[i]) <= 7600 and radius[i] < 50:
#cv2.drawContours(skin, contours_poly, i, (255,0,0))
cv2.circle(skin, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), (0,0,255), 2)
#cv2.imshow('Contours', skin)
cv2.imshow('skin', skin)
#print((centers[i][0]))S
#xe=np.arange(1,121)
#print(len(xe))
#plt.plot(x,centers[i][0],'ro')
#plt.ylabel('some numbers')
#plt.show()
#cv2.imshow('Skin Mask', skinMask)
#cv2.imshow('Skin', skin)
#vcat = cv2.hconcat((skinMask, skin))
#cv2.imshow('vcat', vcat)
#cv2.imshow('hsv_img', hsv_img)
#cv2.imshow('Extracting Saturation', img_s)
#cv2.imshow('img_s_binary1', img_s_binary1)
#cv2.imshow('img_croped', img_croped)
#cv2.imshow('edges', edges)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cv2.destroyAllWindows()
#xc,yc,wc,hc,skin,skinMask,hsv_img,img_s_blur,img_s_binary1,img_croped,edges,cropeedIMAGE