def main():
charDetector = hog.HOG()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('s', gray)
charDetector.describe(gray)
if cv2.waitKey(1) == ord('q'):
break
def load_data(path):
X = []
set_type = path.split('/')[-3]
class_type = path.split('/')[-2]
count = 1
total = len(glob.glob(path))
for im in glob.glob(path):
print(str(count) + '/' + str(total) + ' —— ' + str(im))
hog_vector = hog.HOG(im)
X.append(hog_vector)
count += 1
X = np.array(X)
return X
def load_data(path):
X = []
set_type = path.split('/')[-3]
class_type = path.split('/')[-2]
for im in glob.glob(path):
print(im)
hog_vector = hog.HOG(im)
print(hog_vector.shape)
X.append(hog_vector)
X = np.array(X)
y = np.ones(
X.shape[0]) if class_type == 'pos' else -1 * np.ones(X.shape[0])
return X, y
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
import numpy as np
import hog
import cv2
import dataset
model = joblib.load("model/svm.cpickle")
hg = hog.HOG(orientations=18,
pixelPerCell=(10, 10),
cellsPerBlock=(1, 1),
trasforme=True)
cap = cv2.VideoCapture(0)
while True:
ret, image = cap.read()
image = cv2.imread("cellphone.png", 1)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 30, 150)
(_, cnt, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnt],
key=lambda x: x[1])
for (c, _) in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w >= 7 and h >= 20:
rio = gray[y:y + h, x:x + w]
_, thresh = cv2.threshold(rio.copy(), 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
thresh = dataset.deskew(thresh, 20)
import hog
import cv2 as cv
# Read in the test image
img = cv.imread("../Phantom.jpg")
img = cv.resize(img, (500, 500))
img2 = cv.imread("../Phantom_Melissa.jpg")
img2 = cv.resize(img2, (467, 350))
img3 = cv.imread("../Pixel_melissa.png")
hog = hog.HOG()
hog.run_hog(img)