No blur but use image

This commit is contained in:
Ruben van de Ven 2019-02-06 01:31:42 +01:00
parent fd76f7a97d
commit 6007282ab3
2 changed files with 26 additions and 8 deletions

View File

@ -27,6 +27,9 @@ import coloredlogs
import argparse import argparse
import multiprocessing import multiprocessing
cur_dir = os.path.dirname(__file__)
argParser = argparse.ArgumentParser(description='Draw a heatmap') argParser = argparse.ArgumentParser(description='Draw a heatmap')
argParser.add_argument( argParser.add_argument(
'--camera', '--camera',
@ -85,8 +88,13 @@ logger = logging.getLogger(__name__)
# im = cv2.imread("headPose.jpg"); # im = cv2.imread("headPose.jpg");
spotSize = (100,100)
spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
spot = spot.resize(spotSize)
spot = np.array(spot)
predictor_path = "shape_predictor_68_face_landmarks.dat"
predictor_path = os.path.join(cur_dir,"shape_predictor_68_face_landmarks.dat")
if args.output_dir: if args.output_dir:
lastMetricsFilename = os.path.join(args.output_dir, 'last_metrics.p') lastMetricsFilename = os.path.join(args.output_dir, 'last_metrics.p')
@ -425,7 +433,8 @@ def captureFacesPoints(i):
# TODO only draw nose line now, so we can change color depending whether on screen or not # TODO only draw nose line now, so we can change color depending whether on screen or not
results = {'currentPoint': currentPoint, 'currentPoints': currentPoints, 'im': im} results = {'currentPoint': currentPoint, 'currentPoints': currentPoints}
results['im'] = im if not args.hide_preview else None
try: try:
pointsQueue.put_nowait(results) pointsQueue.put_nowait(results)
@ -505,15 +514,24 @@ while True:
logger.info("Looking at {} {}".format(point, targetPoint) ) logger.info("Looking at {} {}".format(point, targetPoint) )
# cv2.circle(im, (int(targetPoint[0]), int(targetPoint[1])), 2, (0,255,0), -1) # cv2.circle(im, (int(targetPoint[0]), int(targetPoint[1])), 2, (0,255,0), -1)
# from 1920x1080 to 80x50 # from 1920x1080 to 80x50
miniTargetPoint = (int(targetPoint[0] / 1920 * 80 + 10), int(targetPoint[1] / 1080 * 50 + 60)) if not args.hide_preview:
cv2.circle(im, miniTargetPoint, 2, (0,255,0), -1) miniTargetPoint = (int(targetPoint[0] / 1920 * 80 + 10), int(targetPoint[1] / 1080 * 50 + 60))
cv2.circle(im, miniTargetPoint, 2, (0,255,0), -1)
targetInt = (int(targetPoint[0]), int(targetPoint[1])) targetInt = (int(targetPoint[0]), int(targetPoint[1]))
# check if point fits on screen: # check if point fits on screen:
# if so, measure it # if so, measure it
if targetInt[0] >= 0 and targetInt[1] >= 0 and targetInt[0] < metricsSize[0] and targetInt[1] < metricsSize[1]: if targetInt[0]+spotSize[0] >= 0 and targetInt[1]+spotSize[1] >= 0 and targetInt[0]-spotSize[0] < metricsSize[0] and targetInt[1]-spotSize[0] < metricsSize[1]:
dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True) dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True)
logger.debug("Put metric {},{} in metrix of {},{}".format(targetInt[1],targetInt[0], metricsSize[1], metricsSize[0])) logger.info("Put metric {},{} in metrix of {},{}".format(targetInt[1],targetInt[0], metricsSize[1], metricsSize[0]))
newMetrics[targetInt[1],targetInt[0]] += 1 for sx in range(spotSize[0]):
for sy in range(spotSize[1]):
mx = targetInt[0] + sx - (spotSize[0]-1)/2
my = targetInt[1] + sy - (spotSize[1]-1)/2
if mx >= 0 and my >= 0 and mx < metricsSize[0] and my < metricsSize[1]:
newMetrics[my,mx] += spot[sx,sy] #/ 20
print("MAX",np.max(newMetrics))
# TODO: put in an image of a blurred spot & remove blur action # TODO: put in an image of a blurred spot & remove blur action
# after we collected all new metrics, blur them foor smoothness # after we collected all new metrics, blur them foor smoothness
@ -538,7 +556,7 @@ while True:
# update the heatmap output # update the heatmap output
tm21 = time.time() tm21 = time.time()
# smooth impact of first hits by having at least 0.05 # smooth impact of first hits by having at least 0.05
normalisedMetrics = metrics / (max(.02, np.max(metrics))) normalisedMetrics = metrics / (max(255*4 ,np.max(metrics)))
# convert to colormap, thanks to: https://stackoverflow.com/a/10967471 # convert to colormap, thanks to: https://stackoverflow.com/a/10967471
normalisedMetricsColored = np.uint8(cm.nipy_spectral(normalisedMetrics)*255) normalisedMetricsColored = np.uint8(cm.nipy_spectral(normalisedMetrics)*255)
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_RGB2BGR) normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_RGB2BGR)

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