)
while (True):
# Capture frame-by-frame
ret, frame = video_capture.read()
if (frame is None): break #check for empty frames
#Return the binary mask from the backprojection algorithm
frame_mask = my_back_detector.returnMask(frame,
morph_opening=True,
blur=True,
kernel_size=5,
iterations=2)
if (my_mask_analyser.returnNumberOfContours(frame_mask) > 0
and ENABLE_CAPTURE == True):
x_center, y_center = my_mask_analyser.returnMaxAreaCenter(frame_mask)
x_rect, y_rect, w_rect, h_rect = my_mask_analyser.returnMaxAreaRectangle(
frame_mask)
area = w_rect * h_rect
cv2.circle(frame, (x_center, y_center), 3, [0, 255, 0], 5)
#Check the position of the target and press the keys
#KEY_UP, KEY_DOWN, KEY_RIGHT, KEY_LEFT, KEY_SPACE
#KEY_W, KEY_S, KEY_D, KEY_A
#DOWN
if (y_center > int(cam_h / 2) + offset and area > 10000):
ui.write(e.EV_KEY, e.KEY_DOWN, 1)
print("KEY_DOWN")
#UP
elif (y_center < int(cam_h / 2) - offset and area > 10000):
ui.write(e.EV_KEY, e.KEY_UP, 1)
print("KEY_UP")
else:
print("WAITING")
noise_probability = 0.15 #in range [0, 1.0]
while(True):
# Capture frame-by-frame
ret, frame = video_capture.read()
if(frame is None): break #check for empty frames
#Return the binary mask from the backprojection algorithm
frame_mask = my_back_detector.returnMask(frame, morph_opening=True, blur=True, kernel_size=5, iterations=2)
if(my_mask_analyser.returnNumberOfContours(frame_mask) > 0):
#Use the binary mask to find the contour with largest area
#and the center of this contour which is the point we
#want to track with the particle filter
x_rect,y_rect,w_rect,h_rect = my_mask_analyser.returnMaxAreaRectangle(frame_mask)
x_center, y_center = my_mask_analyser.returnMaxAreaCenter(frame_mask)
#Adding noise to the coords
coin = np.random.uniform()
if(coin >= 1.0-noise_probability):
x_noise = int(np.random.uniform(-300, 300))
y_noise = int(np.random.uniform(-300, 300))
else:
x_noise = 0
y_noise = 0
x_rect += x_noise
y_rect += y_noise
x_center += x_noise
y_center += y_noise
cv2.rectangle(frame, (x_rect,y_rect), (x_rect+w_rect,y_rect+h_rect), [255,0,0], 2) #BLUE rect
cv2.moveWindow('Video', 20, 20)
is_first_frame = True
while (True):
# Capture frame-by-frame
ret, frame = video_capture.read()
frame_mask = my_motion_detector.returnMask(frame)
#Uncomment if you want more information about the frame with
#with the largest area.
#cx, cy = my_mask_analyser.returnMaxAreaCenter(frame_mask)
#cnt = my_mask_analyser.returnMaxAreaContour(frame_mask)
if (my_mask_analyser.returnNumberOfContours(frame_mask) > 0):
x, y, w, h = my_mask_analyser.returnMaxAreaRectangle(frame_mask)
cv2.rectangle(frame, (x, y), (x + w, y + h), [0, 255, 0], 2)
#Writing in the output file
out.write(frame)
#Showing the frame and waiting
# for the exit command
if (frame is None): break #check for empty frames
cv2.imshow('Video', frame) #show on window
if cv2.waitKey(1) & 0xFF == ord('q'): break #Exit when Q is pressed
#Release the camera
video_capture.release()
print("Bye...")
cv2.moveWindow('Video', 20, 20)
is_first_frame = True
while(True):
# Capture frame-by-frame
ret, frame = video_capture.read()
frame_mask = my_motion_detector.returnMask(frame)
#Uncomment if you want more information about the frame with
#with the largest area.
#cx, cy = my_mask_analyser.returnMaxAreaCenter(frame_mask)
#cnt = my_mask_analyser.returnMaxAreaContour(frame_mask)
if(my_mask_analyser.returnNumberOfContours(frame_mask) > 0):
x,y,w,h = my_mask_analyser.returnMaxAreaRectangle(frame_mask)
cv2.rectangle(frame, (x,y), (x+w,y+h), [0,255,0], 2)
#Writing in the output file
out.write(frame)
#Showing the frame and waiting
# for the exit command
if(frame is None): break #check for empty frames
cv2.imshow('Video', frame) #show on window
if cv2.waitKey(1) & 0xFF == ord('q'): break #Exit when Q is pressed
#Release the camera
video_capture.release()
print("Bye...")