rectangles = []
counter = 1
scaleFactor = 1.25
scale = 1
font = cv2.FONT_HERSHEY_PLAIN
for resized in pyramid(img, scaleFactor):
scale = float(img.shape[1]) / float(resized.shape[1])
for (x, y, roi) in sliding_window(resized, 20, (100, 40)):
if roi.shape[1] != w or roi.shape[0] != h:
continue
try:
bf = bow_features(roi, extractor, detect)
_, result = svm.predict(bf)
a, res = svm.predict(bf,
flags=cv2.ml.STAT_MODEL_RAW_OUTPUT
| cv2.ml.STAT_MODEL_UPDATE_MODEL)
print("Class: {}, Score: {}, a: {}".format(result[0][0], res[0][0],
res))
score = res[0][0]
if result[0][0] == 1:
if score < -1.0:
rx, ry, rx2, ry2 = int(x * scale), int(y * scale), int(
(x + w) * scale), int((y + h) * scale)
rectangles.append([rx, ry, rx2, ry2, abs(score)])
except:
pass
#img = cv2.imread(test_image)
rectangles = []
counter = 1
scaleFactor = 1.25
scale = 1
font = cv2.FONT_HERSHEY_PLAIN
for resized in pyramid(img, scaleFactor):
scale = float(img.shape[1]) / float(resized.shape[1])
for (x, y, roi) in sliding_window(resized, 20, (100, 40)):
if roi.shape[1] != w or roi.shape[0] != h:
continue
try:
bf = bow_features(roi, extractor, detect)
_, result = svm.predict(bf)
a, res = svm.predict(bf, flags=cv2.ml.STAT_MODEL_RAW_OUTPUT | cv2.ml.STAT_MODEL_UPDATE_MODEL)
print "Class: %d, Score: %f, a: %s" % (result[0][0], res[0][0], res)
score = res[0][0]
if result[0][0] == 1:
if score < -1.0:
rx, ry, rx2, ry2 = int(x * scale), int(y * scale), int((x+w) * scale), int((y+h) * scale)
rectangles.append([rx, ry, rx2, ry2, abs(score)])
except:
pass
counter += 1
windows = np.array(rectangles)
boxes = nms(windows, 0.25)
