def upload_pic(path, layout):
filet = path
fl = path.split(".")[1]
new_img = '/sdcard/' + fl + '_new.png'
#pic=droid.cameraInteractiveCapturePicture(filet)
res = urllib.urlopen(filet)
image = np.asarray(bytearray(res.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
# perform the actual resizing of the image according to scaling_factor
height, width = image.shape[:2]
resized = cv2.resize(image, (width / 3, height / 3),
interpolation=cv2.INTER_AREA)
cv2.imwrite(new_img, resized)
# Return gray sale image
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print resized
layout.views.result.text = "Processing ..."
layout.views.preview.src = "file://" + new_img
time.sleep(3)
args = [
'tesseract', '--tessdata-dir', '/data/data/com.termux/files/usr/share',
new_img, 'stdout', '-l', 'eng', '--psm', '3', '--oem', '2'
]
return run(args)
def main(argv):
default_file = 'images/board.JPEG'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
print('Error opening image!')
print('Usage: hough_lines.py [image_name -- default ' + default_file +
'] \n')
return -1
dst = cv.Canny(src, 50, 200, None, 3)
# Copy edges to the images that will display the results in BGR
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
cdstP = np.copy(cdst)
lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
if lines is not None:
for i in range(0, len(lines)):
rho = lines[i][0][0]
theta = lines[i][0][1]
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (int(x0 + 1000 * (-b)), int(y0 + 1000 * (a)))
pt2 = (int(x0 - 1000 * (-b)), int(y0 - 1000 * (a)))
cv.line(cdst, pt1, pt2, (0, 0, 255), 3, cv.LINE_AA)
linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
if linesP is not None:
for i in range(0, len(linesP)):
l = linesP[i][0]
cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3,
cv.LINE_AA)
y = int(round(1536 / 3))
x = int(round(2048 / 3))
src = cv.resize(cdst, (x, y)) # Resize image
cdst = cv.resize(cdst, (x, y)) # Resize image
cdstP = cv.resize(cdstP, (x, y)) # Resize image
cv.imshow("Source", src)
cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
cv.imwrite("./houghLines.png", cdstP)
cv.waitKey()
return 0
def detect_face(path, layout):
image = cv2.imread(path)
height, width = image.shape[:2]
resized = cv2.resize(image, (width / 5, height / 5))
faces = faceCascade.detectMultiScale(resized,
scaleFactor=1.2,
minNeighbors=5,
minSize=(5, 5),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
(x, y, w, h) = faces[0]
rect = faces[0]
draw_rectangle(resized, rect)
draw_text(resized, "Face", rect[0], rect[1] - 5)
cv2.imwrite("/sdcard/face.jpg", resized)
layout.views.preview.src = "file:///sdcard/face.jpg"
return True
def segment_box(image_Edge,contours,type_):
image_with_MinRect=draw_Min_Rect(image_Edge,contours)
contours_Ang,box_ang=Angle_filter(contours)
contours_Rect,box_rect=Rect_filter(contours_Ang)
#save cpa
for i in box_rect():
CutImage(image,box_rect)
image_Unisize=Unisize(CutImage)
path_tmp=args["tmp_path"]+name
cv.imwrite(path_tmp,image_Unisize)
#SVM classification
svm= SVM_(type_)
for i in box_rect():
CutImage(image,box_rect)
image_Unisize=Unisize(CutImage)
svm.predict(Unisize_image)
if result ==Has:
image_located=Unisize_image
break
return iamge_located #only one image should be output
import cv
from androidhelper import Android
droid = Android()
path = "/sdcard/qpython/photos/test.jpg"
droid.cameraCapturePicture(path)
#droid.cameraInteractiveCapturePicture(path)
img = cv.imread(path)
cv.imwrite('/sdcard/qpython/photos/cvImage.jpg', img)
import cv #to use relative path, note that the working directory is the main qpython folder #to use absolute path, just copy and paste the path from your file manager srcPath = 'great.jpg' desPath = 'call.jpg' img = cv.imread(srcPath, 0) cv.imwrite(desPath, img)
cv.imshow('window,img)
def track_y(y):
cv.line(img, (x, 0), (x, h), RED, d)
cv.line(img, (0, y), (w, y), RED, d)
cv.imshow('window,img)
file = 'messi.jpg'
# img = cv.imread(file, cv.IMREAD_GRAYSCALE)
img0 = cv.imread(file, cv.IMREAD_COLOR)
cv.imshow('window,img)
w, h = 800, 600
x, y = 100, 100
d = 1
cv.createTrackbar('x', 'window, x, w, track_x)
cv.createTrackbar('y', 'window, y, h, track_y)
cv.line(img, (x, 0), (x, h), RED, d)
cv.line(img, (0, y), (w, y), RED, d)
cv.imshow('window,img)
k = cv.waitKey(0)
print('key', k)
cv.imwrite('messigray.png',img)
cv.destroyAllWindows()
import cv as cv
import numpy as np
def normalize(img):
minimum = np.amin(img)
return (img - minimum) * (255 / np.amax(img))
out_path = "result/4.1.2/"
baboon = cv.imread('image/Baboon.bmp', cv.IMREAD_GRAYSCALE)
f16 = cv.imread('image/F16.bmp', 0)
lena = cv.imread('image/Lena.bmp', 0)
cv.imwrite(out_path + 'baboon_gray.jpg', baboon)
cv.imwrite(out_path + 'f16_gray.jpg', f16)
cv.imwrite(out_path + 'lena_gray.jpg', lena)
# compute DFT of images
baboon_f = np.abs(np.fft.fft2(baboon))
f16_f = np.abs(np.fft.fft2(f16))
lena_f = np.abs(np.fft.fft2(lena))
# shift
baboon_f_shift = np.fft.fftshift(baboon_f)
f16_f_shift = np.fft.fftshift(f16_f)
lena_f_shift = np.fft.fftshift(lena_f)
# log on no shifted image
baboon_f_log = np.log(baboon_f + 1)
f16_f_log = np.log(f16_f + 1)
if __name__ == '__main__':
args, conf=pre()
#load the image
# grab the image paths and randomly shuffle them
image_paths = sorted(list(paths.list_images(args['path'])))
#begin to process
for i in image_paths: # i is the path of single image
iamge=cv.imread(i)
name=os.path.split(i)[1]#split path to be name[1] and direction[0]
image_located=locate_mechanism(image,name)
#save the results
path_out=args["output_path"]+name
cv.imwrite(path_out,image_located)
out = img.resize([int(reduced_percent * s) for s in img.size])
imagePath = "/sdcard/image.jpg"
out.save(imagePath)
sleep(3)
# Set the haarcascade file path
cascPath = "/sdcard/haarcascade_frontalface_default.xml"
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
# Read the image
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
print("Found {0} faces!".format(len(faces)))
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
#save the image
cv2.imwrite('/sdcard/out.jpg', image)
#display the image
droid.view("file:///sdcard/out.jpg", "image/*")
return droid, uuid
path = '/storage/7E9B-5A00/Picture/'
#serial通信オープン
droid, uuid = serialStart()
cnt = 0
while True:
# 写真を取る
droid.cameraCapturePicture(path + str(cnt) + 'image.png')
# openCVで開く
img = cv.imread(path + str(cnt) + 'image.png')
mask = red_detect(img) # 赤色検出
cv.imwrite(path + str(cnt) + 'Redmask.png', mask) # 保存
# 面積を求める
pix_area, per = calc_area(mask)
# 重心を求める
x, y = calc_center(mask)
print("G({},{})".format(x, y))
# 差分を取る
if cnt >= 1:
# 1つ前の画像の計算
img0 = cv.imread(path + str(cnt - 1) + 'image.png')
mask0 = red_detect(img0)
_, per0 = calc_area(mask0)
x0, y0 = calc_center(mask0)
print("G0({},{})".format(x0, y0))
import numpy as np
import cv
# Create a black image
#img = np.zeros((512,512,3), np.uint8)
img = cv.imread('great.jpg', 1)
# Draw a diagonal blue line with thickness of 5 px
img = cv.line(img,(0,0),(511,511),(255,0,0),5)
cv.imwrite('blessed.jpg', img)
import sys
# Get user supplied values
imagePath = '/sdcard/cc.png'
cascPath = "/sdcard/haarcascade_frontalface_default.xml"
# Create the haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
# Read the image
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)
print("Found {0} faces!".format(len(faces)))
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imwrite('/sdcard/capture_face_detect.jpg', image)
#display the image
droid.view("file:///sdcard/capture_face_detect.jpg","image/*")
(new_height, new_width))
# shift
img_filtering = np.fft.ifftshift(img_filtering)
# IDFT and real
img_ifft = np.real(np.fft.ifft2(img_filtering))
# resize image
final_img = np.zeros((height, width))
for i in range(height):
for j in range(width):
final_img[i][j] = img_ifft[i][j]
return final_img
out_path = "result/4.1.1/"
barbara_gray = cv.imread('image/Barbara.bmp', cv.IMREAD_GRAYSCALE)
cv.imwrite(out_path + 'barbara_gray.jpg', barbara_gray)
# a filter
a = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])
a = a / 16
# separate a
a_separate_c = np.array([1, 2, 1])
a_separate_c = a_separate_c / 4
a_separate_r = np.array([[1, 2, 1]])
a_separate_r = a_separate_r / 4
# b filter
b = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
# c filter
