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modularized image recognition further

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Pavel Lutskov
2018-05-26 14:40:17 +02:00
parent 0377240382
commit 97bc4cf610
1 changed files with 62 additions and 50 deletions
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112
scripts/live_recognition.py
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@@ -2,16 +2,16 @@ from __future__ import print_function
from __future__ import division
import cv2
from naoqi import ALProxy
from collections import deque
import numpy as np
import imutils
from naoqi import ALProxy
from collections import deque
# Nao configuration
nao_ip = '192.168.0.11'
nao_port = 9559
res = (3, (960, 1280)) # NAOQi code and acutal resolution
res = (2, (480, 640)) # NAOQi code and acutal resolution
fps = 1
cam_id = 0 # 0 := top, 1 := bottom
@@ -19,10 +19,10 @@ cam_id = 0 # 0 := top, 1 := bottom
red_lower = (0, 17, 225) # HSV coded red interval
red_upper = (42, 255, 255)
min_radius = 10
resized_width = 600
resized_width = 600 # Maybe we need it maybe don't (None if don't)
def get_frame(cam_proxy, subscriber):
def get_frame_nao(cam_proxy, subscriber):
result = cam_proxy.getImageRemote(subscriber)
cam_proxy.releaseImage(subscriber)
if result == None:
@@ -30,26 +30,25 @@ def get_frame(cam_proxy, subscriber):
elif result[6] == None:
raise ValueError('no image data string')
else:
# create image
image = np.zeros((res[1][0], res[1][1], 3), np.uint8)
values = map(ord, list(result[6]))
i = 0
for y in range(res[1][0]):
for x in range(res[1][1]):
image.itemset((y, x, 0), values[i + 0])
image.itemset((y, x, 1), values[i + 1])
image.itemset((y, x, 2), values[i + 2])
i += 3
return image
return np.frombuffer(result[6], dtype=np.uint8).reshape(
res[1][0], res[1][1], 3
)
# i = 0
# for y in range(res[1][0]):
# for x in range(res[1][1]): # columnwise
# image.itemset((y, x, 0), values[i + 0])
# image.itemset((y, x, 1), values[i + 1])
# image.itemset((y, x, 2), values[i + 2])
# i += 3
# return image
def find_red_ball(frame):
def find_colored_ball(frame, hsv_lower, hsv_upper, min_radius):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# construct a mask for the color "green", then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, red_lower, red_upper)
# construct a mask for the color "green", then perform a series of
# dilations and erosions to remove any small blobs left in the mask
mask = cv2.inRange(hsv, hsv_lower, hsv_upper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
@@ -62,9 +61,8 @@ def find_red_ball(frame):
if len(cnts) == 0:
return None
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
# find the largest contour in the mask, then use it to compute
# the minimum enclosing circle and centroid
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
@@ -76,43 +74,57 @@ def find_red_ball(frame):
return center, radius
if __name__ == '__main__':
def draw_ball_markers(frame, center, radius, history):
# draw the enclosing circle and ball's centroid on the frame,
cv2.circle(frame, center, radius, (0, 255, 255), 1)
cv2.circle(frame, center, 5, (0, 255, 0), -1)
# loop over the set of tracked points
for i in range(1, len(history)):
# if either of the tracked points are None, ignore them
if history[i - 1] is None or history[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(64 / float(i + 1)) * 2.5)
cv2.line(frame, history[i - 1], history[i], (0, 0, 255), thickness)
return frame
def nao_demo():
vd_proxy = ALProxy('ALVideoDevice', nao_ip, nao_port)
cam_subscriber = vd_proxy.subscribeCamera(
"ball_finder", cam_id, res[0], 13, fps
)
pts = deque(maxlen=64)
history = deque(maxlen=64)
try:
while True:
frame = get_frame(vd_proxy, cam_subscriber)
# resize the frame, blur it
frame = imutils.resize(frame, width=resized_width)
# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
frame = get_frame_nao(vd_proxy, cam_subscriber)
# maybe resize the frame, maybe blur it
if resized_width is not None:
frame = imutils.resize(frame, width=resized_width)
# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
try:
center, radius = find_red_ball(frame)
center, radius = find_colored_ball(
frame, red_lower, red_upper, min_radius
)
except TypeError: # No red ball found and function returned None
pts.appendleft(None)
history.appendleft(None)
continue
history.appendleft(center)
draw_ball_markers(frame, center, radius, history)
# draw the circle and centroid on the frame,
cv2.circle(frame, center, radius, (0, 255, 255), 1)
cv2.circle(frame, center, 5, (0, 255, 0), -1)
pts.appendleft(center)
# loop over the set of tracked points
for i in range(1, len(pts)):
# if either of the tracked points are None, ignore them
if pts[i - 1] is None or pts[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(64 / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the frame to our screen
# show the frame to screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
cv2.waitKey(1)
finally:
vd_proxy.unsubscribe(cam_subscriber)
print(vd_proxy.unsubscribe(cam_subscriber))
if __name__ == '__main__':
nao_demo()