def frame_diff(prev_frame, cur_frame, next_frame):
# Difference between the current frame and the next frame
diff_frames_1 = cv.absdiff(next_frame, cur_frame)
# Difference between the current frame and the previous frame
diff_frames_2 = cv.absdiff(cur_frame, prev_frame)
return cv.bitwise_and(diff_frames_1, diff_frames_2)
def getClosestPlayerByIcon(image, icons, background_bgr):
empty_icon = numpy.full_like(image, background_bgr[0])
empty_icon[:,:,1] = numpy.full((image.shape[0], image.shape[1]), background_bgr[1])
empty_icon[:,:,2] = numpy.full((image.shape[0], image.shape[1]), background_bgr[2])
distances = [numpy.sum(cv2.absdiff(empty_icon, image))]
for i in range(len(icons)):
character_distances = []
for j in range(len(icons[i])):
curr_icon = addBackground(icons[i][j], background_bgr)
character_distances.append(numpy.sum(cv2.absdiff(curr_icon, image)))
distances.append(numpy.min(character_distances))
return numpy.argmin(distances) - 1
def frame_diff(prev_frame, cur_frame, next_frame):
diff_frames_1 = cv.absdiff(
next_frame, cur_frame
) # absdiff(src1, src2), src는 array or scalar, absdiff는 absoulute diffent로 절대값을 비교
diff_frames_2 = cv.absdiff(cur_frame, prev_frame)
return_diff = cv.absdiff(diff_frames_1, diff_frames_2)
threshold = len(
return_diff[np.where(return_diff > 2)]
) # np.where is return elements chosen from x or y depending on condition
if threshold > 500:
print('threshold > 200 : ', threshold)
return return_diff, diff_frames_1
def frame_diff(prev_frame, cur_frame, next_frame):
# Difference between the current frame and the next frame
diff_frames_1 = cv.absdiff(next_frame, cur_frame)
# Difference between the current frame and the previous frame
diff_frames_2 = cv.absdiff(cur_frame, prev_frame)
# return_diff = cv.bitwise_and(diff_frames_1, diff_frames_2)
return_diff = cv.absdiff(diff_frames_1, diff_frames_2)
threshold = len(return_diff[np.where(return_diff > 2)])
if threshold > 500:
print('threshold > 200 : ', threshold)
return return_diff
def detect(self, image, tVal=25):
# compute the absolute difference between the background model
# and the image passed in, then threshold the delta image
delta = cv2.absdiff(self.bg.astype("uint8"), image)
thresh = cv2.threshold(delta, tVal, 255, cv2.THRESH_BINARY)[1]
# perform a series of erosions and dilations to remove small
# blobs
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)
# find contours in the thresholded image and initialize the
# minimum and maximum bounding box regions for motion
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
(minX, minY) = (np.inf, np.inf)
(maxX, maxY) = (-np.inf, -np.inf)
# if no contours were found, return None
if len(cnts) == 0:
return None
# otherwise, loop over the contours
for c in cnts:
# compute the bounding box of the contour and use it to
# update the minimum and maximum bounding box regions
(x, y, w, h) = cv2.boundingRect(c)
(minX, minY) = (min(minX, x), min(minY, y))
(maxX, maxY) = (max(maxX, x + w), max(maxY, y + h))
# otherwise, return a tuple of the thresholded image along
# with bounding box
return (thresh, (minX, minY, maxX, maxY))
def _get_diff_rc_color(
self, examined_img_index: int,
possibilities: List[int]) -> Tuple[int, int, np.array, np.array]:
"""
pick a random image from <possibilities> that is different than <examined_img>,
compare those two images and return a random coordinate where they have different colors. The color of <examed_img> at that coordinate.
:param examined_img_index: the index of the examined image in self._possible_images
:param possibilities: a list of indexes of the compared images
:return: row, col, color, color
"""
that_index = -1
for possibility in possibilities:
if possibility != examined_img_index:
that_index = possibility
assert that_index != -1
this_img = self._possible_images[examined_img_index]
that_img = self._possible_images[that_index]
absolute_diff = cv2.absdiff(this_img, that_img)
mask = np.any(absolute_diff != [0, 0, 0], axis=-1)
nonzero_r_c = np.where(mask)
# if not np.any(nonzero_r_c):
# cv2.imshow('this', this_img)
# cv2.imshow('that', that_img)
# cv2.waitKey()
# cv2.destroyAllWindows()
assert np.any(nonzero_r_c), "Got identical images"
diff_r, diff_c = nonzero_r_c[0][0], nonzero_r_c[1][0]
this_color = this_img[diff_r][diff_c]
that_color = that_img[diff_r][diff_c]
return diff_r, diff_c, this_color, that_color
def pixel_difference(imgA: Image, imgB: Image, threshold: int = 0) -> float:
height, width, _ = imgA.shape
diffImg = cv2.absdiff(imgA, imgB)
diffImg = cv2.cvtColor(diffImg, cv2.COLOR_BGR2GRAY)
_, diffImg = cv2.threshold(diffImg, threshold, 255, cv2.THRESH_BINARY)
numDiffPixels = cv2.countNonZero(diffImg)
return numDiffPixels / (width * height)
def transform(self, filename: str):
self.initialize_video(filename)
if (self.every is not None):
new_idx = range(self.n_frames)[::self.every]
elif (self.hertz is not None):
interval = self.fps / float(self.hertz)
new_idx = np.arange(0, self.n_frames, interval).astype(int)
new_idx = list(new_idx)
elif self.top_n is not None:
diffs = []
for i, img in enumerate(range(self.n_frames)):
if i == 0:
last = img
continue
pixel_diffs = cv2.sumElems(
cv2.absdiff(self.get_frame(last), self.get_frame(img)))
diffs.append(sum(pixel_diffs))
last = img
new_idx = sorted(range(len(diffs)),
key=lambda i: diffs[i],
reverse=True)[:self.top_n]
result = []
for index in new_idx:
result.append(self.get_frame(index))
return result
def detect_contours(self, frame, firstFrame):
gray = self.preprocess(frame)
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
return imutils.grab_contours(cnts)
def getClosestCharacterByCloseup(image, character_closeups, mask, background_bgr):
distances = []
for i in range(len(character_closeups)):
character_distances = []
for j in range(8):
curr_closeup = addBackground(character_closeups[i][j], background_bgr)
character_distances.append(numpy.sum(numpy.multiply(cv2.absdiff(curr_closeup, image), mask)))
distances.append(numpy.min(character_distances))
return numpy.argmin(distances)
def get_images_diff(frames_path: str, image_list: list, idx: int):
img1 = cv2.imread(os.path.join(frames_path, image_list[idx]), 0)
img2 = cv2.imread(os.path.join(frames_path, image_list[idx + 1]), 0)
# --- take the absolute difference of the images ---
res = cv2.absdiff(img1, img2)
# --- convert the result to integer type ---
res = res.astype(numpy.uint32)
# --- find percentage difference based on number of pixels that are not zero ---
return (numpy.count_nonzero(res) * 100) / res.size
def diff(img1: np.ndarray, img2: np.ndarray) -> np.ndarray:
"""
difference between two images to detect changes over time
Args:
img1: image as numpy array
img2: image as numpy array
Returns:
combined image
"""
return cv2.absdiff(img1, img2)
def CompareFrames(self, prevFrame, frame):
frameDelta = cv2.absdiff(self.compareFrame, frame)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
boxes = self.GetBoxes(thresh)
for box in boxes:
cv2.rectangle(frame, (box.x, box.y),
(box.x + box.w, box.y + box.h), (0, 255, 0), 2)
return (frameDelta, thresh, boxes)
def callback(self, data):
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
# (rows, cols, channels) = cv_image.shape
# if cols > 60 and rows > 60:
# cv2.circle(cv_image, (50, 50), 10, 255)
# cv2.imshow("Image window", cv_image)
# cv2.waitKey(3)
if (self.set_s == 0):
self.prev_cv_image = cv_image.copy()
self.set_s = 1
binary = cv_image.copy()
# print("Here Once")
curr_gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
prev_gray = cv2.cvtColor(self.prev_cv_image, cv2.COLOR_BGR2GRAY)
frame_diff = cv2.absdiff(curr_gray, prev_gray)
ret, thresh = cv2.threshold(frame_diff, 30, 255, cv2.THRESH_BINARY)
kernel = np.ones((3, 3), np.uint8)
dilated = cv2.dilate(thresh, kernel, iterations=1)
cnts = cv2.findContours(dilated.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
ctemp_c = cv_image.copy()
valid_cntr = []
for c in cnts:
#(x, y, w, h) = cv2.boundingRect(c)
M = cv2.moments(c)
self.cX = int(M["m10"] / M["m00"])
self.cY = int(M["m01"] / M["m00"])
cv2.drawContours(ctemp_c, cnts, -1, (127, 200, 0), 2)
cv2.circle(ctemp_c, (self.cX, self.cY), 7, (255, 255, 255), -1)
print("center", self.cX, self.cY)
# Detect blobs(groups of pixels)
# image = self.DetectBlobs(morphed, cv_image) # Detect groups using countour area (Green formula)
cv2.imshow("Image window", cv_image)
cv2.imshow('frame diff ', frame_diff)
cv2.imshow("contour", ctemp_c)
cv2.waitKey(1)
self.prev_cv_image = cv_image.copy()
def segment(image):
global bg
global number_of_frames_threshold
diff = cv2.absdiff(bg.astype("uint8"), image)
threshold_image = cv2.threshold(diff, number_of_frames_threshold, 255, cv2.THRESH_BINARY)[1]
contours, hierarchy = cv2.findContours(threshold_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
return
else:
segmented_image = max(contours, key=cv2.contourArea)
return (threshold_image, segmented_image)
def change(self, image, mode=0, gaussian=5, valve=100):
'''
mode return
0 带阀值限制后的输出
1 未使用阀值过滤的原始差异结果
2 带边框指示变化区域的原始图
gaussian 高斯模糊的程度
valve 过滤阀值
'''
# 创建参数解析器并解析参数
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a",
"--min-area",
type=int,
default=500,
help="minimum area size")
args = vars(ap.parse_args())
# 高斯模糊，防噪点
image = cv2.GaussianBlur(image, (gaussian, gaussian), 0)
# 计算当前帧和第一帧的不同
frameDelta = cv2.absdiff(self.__init_frame, image)
thresh = cv2.threshold(frameDelta, valve, 255, cv2.THRESH_BINARY)[1]
if mode == 0:
#cv2.imshow("Thresh", thresh)
return thresh
elif mode == 1:
#cv2.imshow("Frame Delta", frameDelta)
return frameDelta
elif mode == 2:
# 创建边框
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
if cv2.contourArea(c) < args["min_area"]:
continue
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# 输出带矩形标记的当前帧
cv2.imshow("Security Feed", image)
return image
else:
raise (ValueError)
def frame_diff(self):
_, self.previous_frame = self.video_capture.read()
while (True):
_, self.current_frame = self.video_capture.read()
# compute the absolute difference between the current frame and previous frame
# convert it to grayscale, and blur it
diff = cv2.absdiff(self.previous_frame, self.current_frame)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (self.kernel_size, self.kernel_size),
0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
# dilate the thresholded image to fill in holes, then find contours on thresholded image
dilated = cv2.dilate(thresh, None, iterations=3)
contours, _ = cv2.findContours(dilated, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
movement_flag = False
for contour in contours:
if cv2.contourArea(contour) < self.min_area:
continue
movement_flag = True
show_frame = np.copy(self.current_frame)
if self.lot_of_noise_det:
white_pixels_count = np.sum(dilated)
if not movement_flag and white_pixels_count < self.mov_detected_pixels_threshold:
self.mark_as_removed(show_frame)
else:
self.video_writer.write(self.current_frame)
else:
if not movement_flag:
self.mark_as_removed(show_frame)
else:
self.video_writer.write(self.current_frame)
# cv2.imshow("Live Contours", self.previous_frame)
cv2.drawContours(show_frame, contours, -1, (0, 255, 0), 2)
cv2.imshow("Live Feed", show_frame)
cv2.imshow("Dilated", dilated)
cv2.imshow("Frame Delta", gray)
self.previous_frame = self.current_frame
if cv2.waitKey(60) & 0xFF == ord('q'):
break
def Shadow_removal(warped): # Funkcija za otklanjanje sjene sa slike
img = warped.copy() # Kopiramo poravnatu sliku pod imenom img
rgb_planes = cv2.split(img) # Razdvajamo boje RGB spektruma
result_norm_planes = [] # Niz u koji spremamo normalizirane boje slike
# Prolazimo kroz sve dijelove boja slike
for plane in rgb_planes:
dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8)) #
bg_img = cv2.medianBlur(dilated_img, 21) # Prolazimo kroz sve 3 boje RGB-a, te
diff_img = 255 - cv2.absdiff(plane, bg_img) # Normaliziramo svaku boju kako bi otklonili sjenu
norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1) #
result_norm_planes.append(norm_img) # Spremamo narmaliziranu boju u niz
result_norm = cv2.merge(result_norm_planes) # Ponovno spajamo sve boje u jednu sliku
result_norm = cv2.cvtColor(result_norm, cv2.COLOR_BGR2GRAY) # Pretvaramo sliku u crno-bijelu
cv2.imwrite('scanned.jpg', result_norm) # Spremamo sliku kojoj smo otklonili sjenu
def ImageSegmentation(frame, count):
# path is the video directory for which we are applying the segementation
# any motion box of less than this size will be a noise #(assumption, to not consider, light changes as motion)
min_size_area = 50
cv2.imshow("frame", frame)
frame_number = count
# if the frame could not be grabbed, then we have reached the end
# of the video
if frame is None:
print("Frame not readed or frame read ends")
return 0
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0) # must be odd
# if the first frame is None, initialize it
if compFrame is None:
compFrame = gray
return 0
# compute the absolute difference between the current frame and
# first frame
frameDelta = cv2.absdiff(compFrame, gray)
if frame_number-prevUpdateCounter == 10:
compFrame = prevFrame
prevUpdateCounter = frame_number
prevFrame = gray
return 0
prevFrame = gray
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(
thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
temp = 0
# loop over the contours
for c in cnts:
if cv2.contourArea(c) < min_size_area:
continue
temp = 1
if temp == 1:
bounding_boxes.append(frame_number)
return 1
return 0
def find_license(img):
'''
预处理函数
'''
m = 400 * img.shape[0] / img.shape[1]
img = cv2.resize(img, (400, int(m)), interpolation=cv2.INTER_CUBIC)
# BGR转换为灰度图像
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 灰度拉伸
stretchedimg = stretch(gray_img)
r = 16
h = w = r * 2 + 1
kernel = np.zeros((h, w), np.uint8)
cv2.circle(kernel, (r, r), r, 1, -1)
# 开运算
openingimg = cv2.morphologyEx(stretchedimg, cv2.MORPH_OPEN, kernel)
# 获取差分图，两幅图像做差 cv2.absdiff('图像1','图像2')
strtimg = cv2.absdiff(stretchedimg, openingimg)
# 图像二值化
binaryimg = dobinaryzation(strtimg)
# canny边缘检测
canny = cv2.Canny(binaryimg, binaryimg.shape[0], binaryimg.shape[1])
# 进行闭运算
kernel = np.ones((5, 19), np.uint8)
closingimg = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernel)
# 进行开运算
openingimg = cv2.morphologyEx(closingimg, cv2.MORPH_OPEN, kernel)
# 再次进行开运算
kernel = np.ones((11, 5), np.uint8)
openingimg = cv2.morphologyEx(openingimg, cv2.MORPH_OPEN, kernel)
# 消除小区域，定位车牌位置
rect = locate_license(openingimg, img)
return rect, img
def preprocessor(img):
gray = img
dilated_gray = cv2.dilate(gray, np.ones((7, 7), np.uint8))
bg_gray = cv2.medianBlur(dilated_gray, 21)
diff_gray = 255 - cv2.absdiff(gray, bg_gray)
norm_gray = diff_gray.copy()
norm_img = np.zeros((320, 240))
norm_gray = cv2.normalize(diff_gray,
norm_img,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_8UC1)
test_img = diff_gray - norm_gray
test_img2 = cv2.dilate(test_img, np.ones((5, 5)))
ret2, gray3 = cv2.threshold(test_img2, 0, 255, cv2.THRESH_OTSU)
gray3 = cv2.resize(gray3, (28, 28), interpolation=cv2.INTER_AREA)
return gray3, gray3.copy()
def segment(image, threshold=50):
global bg
# find the absolute difference between background and current frame
diff = cv2.absdiff(bg.astype("uint8"), image)
# threshold the diff image so that we get the foreground
ret, thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
# get the contours in the thresholded image
cnts, _ = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# return None, if no contours detected
if len(cnts) == 0:
return
else:
# based on contour area, get the maximum contour which is the hand
segmented = max(cnts, key=cv2.contourArea)
return (thresholded, segmented)
def car(frame1, frame2):
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
dilated = cv2.dilate(thresh, None, iterations=3)
contours, _ = cv2.findContours(dilated, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
(x, y, w, h) = cv2.boundingRect(contour)
if cv2.contourArea(contour) < 900:
continue
cv2.rectangle(frame1, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
#cv2.drawContours(frame1,contours, -1, (0,255,0),2)
return frame1
def preprocessor2(img):
gray = img
dilated_gray = cv2.dilate(gray, np.ones((7, 7), np.uint8))
bg_gray = cv2.medianBlur(dilated_gray, 21)
diff_gray = 255 - cv2.absdiff(gray, bg_gray)
norm_gray = diff_gray.copy()
_, thr_gray = cv2.threshold(norm_gray, 253, 0, cv2.THRESH_TRUNC)
norm_img = np.zeros((320, 240))
norm_gray = cv2.normalize(diff_gray,
norm_img,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_8UC1)
test_img = diff_gray - norm_gray
test_img2 = cv2.dilate(test_img, np.ones((5, 5)))
ret2, gray3 = cv2.threshold(test_img2, 40, 255, cv2.THRESH_OTSU)
gray3 = cv2.resize(gray3, (28, 28), interpolation=cv2.INTER_AREA)
ret3, gray4 = cv2.threshold(test_img2, 40, 255, cv2.THRESH_TRUNC)
gray4 = cv2.resize(gray4, (28, 28), interpolation=cv2.INTER_AREA)
gray5 = cv2.resize(thr_gray, (28, 28), interpolation=cv2.INTER_AREA)
_, gray6 = cv2.threshold(norm_gray, 30, 30, cv2.THRESH_OTSU)
#gray6 = cv2.resize(gray6, (28,28), interpolation=cv2.INTER_AREA)
#gray6 = cv2.resize(thr_gray, (28,28), interpolation=cv2.INTER_AREA)
#(ret6, gray6) = cv2.threshold(test_img2, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
#gray6 = cv2.resize(norm_gray, (28,28), interpolation=cv2.INTER_AREA)
#gray = cv2.adaptiveThreshold(gray1, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 5, 3)
#(thresh, gray2) = cv2.threshold(gray1, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
return gray3, gray4, gray5, gray6
def getEffectiveFrame(videopath,dirfile):
# 如果文件目录不存在则创建目录
if not os.path.exists(dirfile):
os.makedirs(dirfile)
(filepath, tempfilename) = os.path.split(videopath)#分离路径和文件名
(filename, extension) = os.path.splitext(tempfilename)#区分文件的名字和后缀
#Setting fixed threshold criteria设置固定阈值标准
USE_THRESH = False
#fixed threshold value固定阈值
THRESH = 0.6
#Setting fixed threshold criteria设置固定阈值标准
USE_TOP_ORDER = False
#Setting local maxima criteria设置局部最大值标准
USE_LOCAL_MAXIMA = True
#Number of top sorted frames排名最高的帧数
NUM_TOP_FRAMES = 50
#smoothing window size平滑窗口大小
len_window = int(50)
print("target video :" + videopath)
print("frame save directory: " + dirfile)
# load video and compute diff between frames加载视频并计算帧之间的差异
cap = cv2.VideoCapture(str(videopath))
curr_frame = None
prev_frame = None
frame_diffs = []
frames = []
success, frame = cap.read()
i = 0
while(success):
luv = cv2.cvtColor(frame, cv2.COLOR_BGR2LUV)
curr_frame = luv
if curr_frame is not None and prev_frame is not None:
#logic here
diff = cv2.absdiff(curr_frame, prev_frame)#获取差分图
diff_sum = np.sum(diff)
diff_sum_mean = diff_sum / (diff.shape[0] * diff.shape[1])#平均帧
frame_diffs.append(diff_sum_mean)
frame = Frame(i, diff_sum_mean)
frames.append(frame)
prev_frame = curr_frame
i = i + 1
success, frame = cap.read()
cap.release()
# compute keyframe
keyframe_id_set = set()
if USE_TOP_ORDER:
# sort the list in descending order以降序对列表进行排序
frames.sort(key=operator.attrgetter("diff"), reverse=True)# 排序operator.attrgetter
for keyframe in frames[:NUM_TOP_FRAMES]:
keyframe_id_set.add(keyframe.id)
if USE_THRESH:
print("Using Threshold")#使用阈值
for i in range(1, len(frames)):
if (rel_change(np.float(frames[i - 1].diff), np.float(frames[i].diff)) >= THRESH):
keyframe_id_set.add(frames[i].id)
if USE_LOCAL_MAXIMA:
print("Using Local Maxima")#使用局部极大值
diff_array = np.array(frame_diffs)
sm_diff_array = smooth(diff_array, len_window)#平滑
frame_indexes = np.asarray(argrelextrema(sm_diff_array, np.greater))[0]#找极值
for i in frame_indexes:
keyframe_id_set.add(frames[i - 1].id)# 记录极值帧数
plt.figure(figsize=(40, 20))
plt.locator_params("x", nbins = 100)
# stem 绘制离散函数，polt是连续函数
plt.stem(sm_diff_array,linefmt='-',markerfmt='o',basefmt='--',label='sm_diff_array')
plt.savefig(dirfile + filename+'_plot.png')
# save all keyframes as image将所有关键帧另存为图像
cap = cv2.VideoCapture(str(videopath))
curr_frame = None
keyframes = []
success, frame = cap.read()
idx = 0
while(success):
if idx in keyframe_id_set:
name = filename+'_' + str(idx) + ".jpg"
cv2.imwrite(dirfile + name, frame)
keyframe_id_set.remove(idx)
idx = idx + 1
success, frame = cap.read()
cap.release()
# Captura de movimento
from cv2 import cv2
import numpy as np
cap = cv2.VideoCapture('vtest.avi')
ret, frame1 = cap.read()
ret, frame2 = cap.read()
while cap.isOpened():
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
dilated = cv2.dilate(thresh, None, iterations=3)
contours, _ = cv2.findContours(dilated, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
(x, y, w, h) = cv2.boundingRect(contour)
if cv2.contourArea(contour) < 900:
continue
cv2.rectangle(frame1, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame1, f"Status: {'Movement'}", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
#cv2.drawContours(frame1, contours, -1, (0, 255, 0), 2)
cv2.imshow("feed", frame1)
frame1 = frame2
ret, frame2 = cap.read()
def getClosestLanguageType(image, languages):
distances = []
for i in range(len(languages)):
distances.append(numpy.sum(cv2.absdiff(languages[i][0], image)))
index = numpy.argmin(distances)
return languages[index][1]
def getClosestDigit(image, digit_images):
distances = []
for i in range(len(digit_images)):
distances.append(numpy.sum(cv2.absdiff(digit_images[i], image)))
digit = numpy.argmin(distances)
return digit - 1
static_mask = previous
continue
(grabbed, frame) = camera.read()
frame = imutils.resize(frame, width=700)
clone = frame.copy()
(height, width) = frame.shape[:2]
#potential speedup if converted to grayscale, the net should intake grayscale as well
grayClone = cv2.cvtColor(clone, cv2.COLOR_BGR2GRAY)
# Calculate absolute difference of current frame and
# the next frame
_diff = cv2.absdiff(grayClone, previous)
num_diff = cv2.countNonZero(_diff)
# Calculate the minimum nonzero pixels in order to update the background
if min_diff is None:
min_diff = np.min(num_diff)
continue
min_diff = np.min(
num_diff) if min_diff > np.min(num_diff) else min_diff
if num_diff > min_diff:
# gives coordinates in (x,y) format. Note that, row = x and column = y.
# mask = cv2.findNonZero(_diff)
# rows = 79844; cols = 1
# pix_mask[tuple(mask.T)] = 0
def frame_diff(prev_frame, cur_frame):
diff_frames = absdiff(prev_frame, cur_frame)
diff_frames_with_cv = cv2.absdiff(prev_frame, cur_frame)
final = np.hstack((cur_frame, diff_frames, diff_frames_with_cv))
cv2.imshow('diff', final)
