def processDatabase(dataset,
names,
deg=0,
scale=1.0,
min_score_thresh=0.7,
showImg=True):
"""run face detection for named dataset as names.
dataset:
names:
deg: angle (anti-clockwise)
"""
if dataset == "headPose":
import readheadPose
d = readheadPose.getTruePosition()
log = open("log_%s_%d_%f.csv" % (dataset, deg, scale), "wt")
log.write("name,num,truePositives,falsePositives,meanSize\n")
tDetector = TensoflowFaceDector()
category_index = getGategoryIndex()
windowNotSet = True
for p in names:
dstDir = "result"
dstname = os.path.join(dstDir, p)
dirname = os.path.dirname(dstname)
if not os.path.isdir(dirname):
os.makedirs(dirname)
frame = cv.imread(p)
if deg != 0:
frame = helper.rotate(frame, deg)
[h, w] = frame.shape[:2]
scaledImg = helper.scaledImage(frame, scale)
frame = scaledImg
cols = frame.shape[1]
rows = frame.shape[0]
[h, w] = frame.shape[:2]
imgCenter = [cols / 2, rows / 2]
(boxes, scores, classes, num_detections) = tDetector.run(frame)
trueDetection = {True: 0, False: 0}
if dataset in ("lfw", ):
center = imgCenter
center = (int(scale * center[0]), int(scale * center[1]))
elif dataset == "headPose":
v = d[p]
center = (v[0], v[1])
center = readheadPose.getRotatedPoint(center, deg, imgCenter)
# ここで縮小したことによる画像の点の扱いを修正すること
center = (int(scale * center[0]), int(scale * center[1]))
r = int(50 * scale)
cv.circle(frame, center, r, (0, 255, 0))
else:
center = imgCenter
center = (int(scale * center[0]), int(scale * center[1]))
trueSizes = []
boxes_shape = boxes.shape
for i in range(boxes_shape[0]):
ymin, xmin, ymax, xmax = boxes[i, 0], boxes[i,
1], boxes[i,
2], boxes[i,
3]
yLeftTop, xLeftTop, yRightBottom, xRightBottom = ymin * h, xmin * w, ymax * h, xmax * w
yLeftTop, xLeftTop, yRightBottom, xRightBottom = int(
yLeftTop), int(xLeftTop), int(yRightBottom), int(xRightBottom)
width = xRightBottom - xLeftTop
if scores[i] <= min_score_thresh:
continue
isPositive = helper.isInside(center, (xLeftTop, yLeftTop),
(xRightBottom, yRightBottom))
trueDetection[isPositive] += 1
trueSizes.append(width)
cv.circle(frame, (xLeftTop, yLeftTop), 5, (0, 255, 0))
cv.circle(frame, (xRightBottom, yRightBottom), 5, (0, 255, 0))
color = {True: (0, 255, 0), False: (0, 0, 255)}[isPositive]
cv.rectangle(frame, (xLeftTop, yLeftTop),
(xRightBottom, yRightBottom), color, 5)
found = trueDetection[True] + trueDetection[False]
log.write("%s, %d, %d, %d, %s\n" %
(p, found, trueDetection[True], trueDetection[False], `
np.mean(trueSizes) `))
if windowNotSet is True:
cv.namedWindow("tensorflow based (%d, %d)" % (w, h),
cv.WINDOW_NORMAL)
windowNotSet = False
if showImg:
cv.imshow("tensorflow based (%d, %d)" % (w, h), frame)
k = cv.waitKey(1) & 0xff
if k == ord('q') or k == 27:
break
log.close()
cv.destroyAllWindows()
def processDatabase(dataset,
names,
deg=0,
scale=1.0,
confThreshold=0.5,
showImg=True):
"""run face detection for named dataset as names.
dataset:
names:
deg: angle (anti-clockwise)
"""
if dataset == "headPose":
import readheadPose
d = readheadPose.getTruePosition()
log = open("log_%s_%d_%f.csv" % (dataset, deg, scale), "wt")
log.write("name,num,truePositives,falsePositives,meanSize\n")
detector = resnetFaceDetector.ResnetFaceDetector()
for p in names:
dstDir = "result"
dstname = os.path.join(dstDir, p)
dirname = os.path.dirname(dstname)
if not os.path.isdir(dirname):
os.makedirs(dirname)
frame = cv.imread(p)
if deg != 0:
frame = helper.rotate(frame, deg)
[h, w] = frame.shape[:2]
scaledImg = helper.scaledImage(frame, scale)
frame = scaledImg
cols = frame.shape[1]
rows = frame.shape[0]
[h, w] = frame.shape[:2]
imgCenter = [cols / 2, rows / 2]
dets, confidences, perf_stats = detector.run(frame, confThreshold)
trueDetection = {True: 0, False: 0}
if dataset in ("lfw", ):
center = imgCenter
center = (int(scale * center[0]), int(scale * center[1]))
elif dataset == "headPose":
v = d[p]
center = (v[0], v[1])
center = readheadPose.getRotatedPoint(center, deg, imgCenter)
# ここで縮小したことによる画像の点の扱いを修正すること
center = (int(scale * center[0]), int(scale * center[1]))
r = int(50 * scale)
cv.circle(frame, center, r, (0, 255, 0))
else:
center = imgCenter
center = (int(scale * center[0]), int(scale * center[1]))
trueSizes = []
for i, det in enumerate(dets):
confidence = confidences[i]
xLeftTop, yLeftTop, width, height = det
xRightBottom = xLeftTop + width
yRightBottom = yLeftTop + height
width = xRightBottom - xLeftTop
isPositive = helper.isInside(center, (xLeftTop, yLeftTop),
(xRightBottom, yRightBottom))
trueDetection[isPositive] += 1
trueSizes.append(width)
cv.circle(frame, (xLeftTop, yLeftTop), 5, (0, 255, 0))
cv.circle(frame, (xRightBottom, yRightBottom), 5, (0, 255, 0))
color = {True: (0, 255, 0), False: (0, 0, 128)}[isPositive]
cv.rectangle(frame, (xLeftTop, yLeftTop),
(xRightBottom, yRightBottom), color)
label = "face: %.4f" % confidence
labelSize, baseLine = cv.getTextSize(label,
cv.FONT_HERSHEY_SIMPLEX, 0.5,
1)
cv.rectangle(frame, (xLeftTop, yLeftTop - labelSize[1]),
(xLeftTop + labelSize[0], yLeftTop + baseLine),
(255, 255, 255), cv.FILLED)
cv.putText(frame, label, (xLeftTop, yLeftTop),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
found = trueDetection[True] + trueDetection[False]
log.write("%s, %d, %d, %d, %s\n" %
(p, found, trueDetection[True], trueDetection[False], `
np.mean(trueSizes) `))
cv.imwrite(dstname, frame)
if showImg:
cv.imshow("resnet based (%d, %d)" % (w, h), frame)
k = cv.waitKey(1) & 0xff
if k == ord('q') or k == 27:
break
log.close()
cv.destroyAllWindows()