def detect_stop_sign(image, boundaryR):
lowerR = np.array(boundaryR[0][0], dtype='uint8')
upperR = np.array(boundaryR[0][1], dtype='uint8')
img = edge_enhancement(image)
alpha = 1.5
beta = 50
res = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
hsv = cv2.cvtColor(res, cv2.COLOR_BGR2HSV)
H, S, V = cv2.split(hsv)
S = S + 50
hsvR = cv2.merge((H, S, V))
kernel = np.ones((3, 3), np.uint8)
maskR = cv2.inRange(hsvR, lowerR, upperR)
maskR = cv2.morphologyEx(maskR, cv2.MORPH_CLOSE, kernel)
maskR = cv2.morphologyEx(maskR, cv2.MORPH_OPEN, kernel)
cntR, max_cntR, approx_cntR = geom.find_main_contour_approx(maskR)
try:
shapeR, thresh = detect(max_cntR, maskR)
if shapeR == 'octagon' and cv2.countNonZero(maskR) > 1000:
cv2.putText(image, 'STOP',
(image.shape[1] - 100, image.shape[0] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
except:
pass
return image
def prepare_stop_pic(image):
img = edge_enhancement(image)
# alpha = 1.5
# beta = 50
# res = cv2.convertScaleAbs(img, alpha = alpha, beta = beta)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# hsv[:,:,1] = hsv[:,:,1] + 50
# H, S, V = cv2.split(hsv)
# S = S + 50
# hsvR = cv2.merge((H, S, V))
return hsv
def prepare_addr_pic(image):
img = edge_enhancement(image)
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
L, A, B = cv2.split(lab)
if np.mean(L) < 120:
img = adjust_gamma(img, gamma=0.6)
L, A, B = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2LAB))
greenA = A.copy()
greenA[A < 90] = 0
greenA[A > 115] = 0
greenB = B.copy()
greenB[B < 140] = 0
greenB[B > 160] = 0
maskG = cv2.bitwise_and(greenA, greenA, mask=greenB)
kernel = np.ones((3, 3), np.uint8)
maskG = cv2.morphologyEx(maskG, cv2.MORPH_OPEN, kernel)
maskG = cv2.morphologyEx(maskG, cv2.MORPH_CLOSE, kernel)
maskG = cv2.morphologyEx(maskG, cv2.MORPH_DILATE, kernel)
return maskG
import cv2
import num_detect.number_geom as geom
from bright.bright_function import edge_enhancement
#################
datafolder = '/Users/soua/Desktop/Project/sterling_demo'
img_path = datafolder + '/Img_89.jpg' # 202 s : 56 -> +50 / v : 104 -> 255 - = 151
# img_path = datafolder + '/Img_284.jpg' # 203 s : 84 -> +50 / v : 81 -> 255 - = 175
# img_path = datafolder + '/Img_390.jpg' # 103 s : 41 -> + 100 / v : 141 -> 255 - = 115
# img_path = datafolder + '/Img_907.jpg' # 101 s : 90 / v : 71 -> 235 - = 164
# img_path = datafolder + '/Img_1398.jpg' # 101 s : 78 / v : 69
# img_path = datafolder + '/Img_1077.jpg' # 201 s : 35 / V : 138
img = cv2.imread(img_path)
img = edge_enhancement(img)
cv2.imshow('before scaling', img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
hist_size = len(hist)
accm = []
accm.append(float(hist[0]))
for idx in range(1, hist_size):
accm.append(accm[idx - 1] + float(hist[idx]))
max = accm[-1]
clip_hist_percent = 1
clip_hist_percent *= (max / 100.0)
clip_hist_percent /= 2.0