def detect_objects_large(frame, mask, fgbg, origin):
masked = cv2.convertScaleAbs(frame, alpha=1, beta=0)
gain = 15
masked = cv2.convertScaleAbs(masked, alpha=1, beta=256-average_brightness(16, frame, mask)+gain)
masked = fgbg.apply(masked, learningRate=-1)
kernel = np.ones((5, 5), np.uint8)
# Remove Noise
masked = cv2.morphologyEx(masked, cv2.MORPH_OPEN, kernel, iterations=int(1))
masked = cv2.dilate(masked, kernel, iterations=int(4*SCALE_FACTOR))
contours, hierarchy = cv2.findContours(masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
n_keypoints = len(contours)
centroids = np.zeros((n_keypoints, 2))
sizes = np.zeros((n_keypoints,2))
for i, contour in enumerate(contours):
M = cv2.moments(contour)
centroids[i] = [int(M['m10']/M['m00']), int(M['m01']/M['m00'])]
centroids[i] += origin
x, y, w, h = cv2.boundingRect(contour)
sizes[i] = (w, h)
return centroids, sizes, masked
def detect_objects(frame, mask, fgbg, detector, origin):
masked = cv2.convertScaleAbs(frame, alpha=1, beta=0)
gain = 15
masked = cv2.convertScaleAbs(masked,
alpha=1,
beta=256 -
average_brightness(16, frame, mask) + gain)
masked = fgbg.apply(masked, learningRate=-1)
masked = remove_ground(masked, int(13 / (2.26 / SCALE_FACTOR)), 0.6, frame)
kernel_dilation = np.ones((5, 5), np.uint8)
masked = cv2.dilate(masked, kernel_dilation, iterations=2)
masked = cv2.bitwise_not(masked)
# keypoints = []
# Blob detection
keypoints = detector.detect(masked)
n_keypoints = len(keypoints)
centroids = np.zeros((n_keypoints, 2))
sizes = np.zeros((n_keypoints, 2))
for i in range(n_keypoints):
centroids[i] = keypoints[i].pt
centroids[i] += origin
sizes[i] = keypoints[i].size
return centroids, sizes, masked
def detect_objects(frame, mask, fgbg, detector, origin):
# Adjust contrast and brightness of image to make foreground stand out more
# alpha used to adjust contrast, where alpha < 1 reduces contrast and alpha > 1 increases it
# beta used to increase brightness, scale of (-255 to 255) ? Needs confirmation
# formula is im_out = alpha * im_in + beta
# Therefore to change brightness before contrast, we need to do alpha = 1 first
masked = cv2.convertScaleAbs(frame, alpha=1, beta=0)
gain = 15
masked = cv2.convertScaleAbs(masked,
alpha=1,
beta=256 -
average_brightness(16, frame, mask) + gain)
# masked = cv2.convertScaleAbs(masked, alpha=2, beta=128)
# masked = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
# masked = threshold_rgb(frame)
imshow_resized("pre-background subtraction", masked)
# Subtract Background
# Learning rate affects how often the model is updated
# High values > 0.5 tend to lead to patchy output
# Found that 0.1 - 0.3 is a good range
masked = fgbg.apply(masked, learningRate=-1)
imshow_resized("background subtracted", masked)
masked = remove_ground(masked, int(13 / (2.26 / SCALE_FACTOR)), 0.6, frame)
# Morphological Transforms
# Close to remove black spots
# masked = imclose(masked, 3, 1)
# Open to remove white holes
# masked = imopen(masked, 3, 2)
# masked = imfill(masked)
kernel_dilation = np.ones((5, 5), np.uint8)
masked = cv2.dilate(masked, kernel_dilation, iterations=2)
# Apply foreground mask (dilated) to the image and perform detection on that
# masked = cv2.bitwise_and(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), masked)
# Invert frame such that black pixels are foreground
masked = cv2.bitwise_not(masked)
# keypoints = []
# Blob detection
keypoints = detector.detect(masked)
n_keypoints = len(keypoints)
centroids = np.zeros((n_keypoints, 2))
sizes = np.zeros((n_keypoints, 2))
for i in range(n_keypoints):
centroids[i] = keypoints[i].pt
centroids[i] += origin
sizes[i] = keypoints[i].size
return centroids, sizes, masked
def detect_objects(frame, mask, fgbg, detector, origin, index, scale_factor):
if average_brightness_hsv(16, frame, mask) > parm.BRIGHTNESS_THRES:
# If sun compensation is required, extract the sky and apply localised contrast increase to it
# And then restore the non-sky (i.e. treeline) back into the image to avoid losing data
masked, non_sky = extract_sky(frame)
masked = cv2.convertScaleAbs(masked, alpha=2, beta=0)
masked = cv2.add(masked, non_sky)
else:
masked = cv2.convertScaleAbs(frame, alpha=1, beta=0)
imshow_resized("pre-backhground subtraction", masked)
masked = cv2.convertScaleAbs(
masked,
alpha=1,
beta=256 - average_brightness(16, frame, mask) + parm.BRIGHTNESS_GAIN)
# masked = cv2.convertScaleAbs(masked, alpha=2, beta=128)
# masked = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
# masked = threshold_rgb(frame)
# Subtract Background
# Learning rate affects how often the model is updated
# High values > 0.5 tend to lead to patchy output
# Found that 0.1 - 0.3 is a good range
masked = fgbg.apply(masked, learningRate=parm.FGBG_LEARNING_RATE)
masked = remove_ground(masked, int(13 / (2.26 / scale_factor)), 0.5, frame,
index)
cv2.imshow("after remove ground", masked)
# Morphological Transforms
# Close to remove black spots
# masked = imclose(masked, 3, 1)
# Open to remove white holes
# masked = imopen(masked, 3, 2)
# masked = imfill(masked)
kernel_dilation = np.ones((5, 5), np.uint8)
masked = cv2.dilate(masked, kernel_dilation, iterations=parm.DILATION_ITER)
# Apply foreground mask (dilated) to the image and perform detection on that
# masked = cv2.bitwise_and(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), masked)
# Invert frame such that black pixels are foreground
masked = cv2.bitwise_not(masked)
cv2.imshow("after dilation again and inversion", masked)
# Blob detection
keypoints = detector.detect(masked)
n_keypoints = len(keypoints)
centroids = np.zeros((n_keypoints, 2))
sizes = np.zeros((n_keypoints, 2))
for i in range(n_keypoints):
centroids[i] = keypoints[i].pt
centroids[i] += origin
sizes[i] = keypoints[i].size
return centroids, sizes, masked
def detect_objects(
self,
frame,
fgbg,
detector,
mask=None,
):
# Adjust contrast and brightness of image to make foreground stand out more
# alpha used to adjust contrast, where alpha < 1 reduces contrast and alpha > 1 increases it
# beta used to increase brightness, scale of (-255 to 255) ? Needs confirmation
# formula is im_out = alpha * im_in + beta
if not self.brightness_override:
gain = 15
masked = cv2.convertScaleAbs(
frame,
alpha=1,
beta=256 - average_brightness(16, frame, mask) + gain)
else:
masked = cv2.convertScaleAbs(frame,
alpha=1,
beta=self.brightness_threshold)
print(self.brightness_threshold)
if self.debug: imshow_resized('Brightness Adjusted', masked)
# Subtract Background
# Learning rate affects how often the model is updated
# High values > 0.5 tend to lead to patchy output
# Found that 0.1 - 0.3 is a good range
masked = fgbg.apply(masked, learningRate=-1)
if self.debug: imshow_resized('Foreground mask', masked)
self.remove_ground(masked, int(13 / (2.26 / self.cap_scaling)), 0.7,
frame)
kernel_dilation = np.ones((5, 5), np.uint8)
masked = cv2.dilate(masked, kernel_dilation, iterations=2)
# Invert frame such that black pixels are foreground
masked = cv2.bitwise_not(masked)
# keypoints = []
# Blob detection
keypoints = detector.detect(masked)
n_keypoints = len(keypoints)
centroids = np.zeros((n_keypoints, 2))
sizes = np.zeros(n_keypoints)
for i in range(n_keypoints):
centroids[i] = keypoints[i].pt
centroids[i] += self.origin
sizes[i] = keypoints[i].size
return masked, centroids, sizes
