def run(self):
global cap, isCameraOpen, isEnd, yolo, results, display, mot_tracker, isDetect
#try:
# import pyrealsense2 as rs
#except:
# print("pyrealsense error")
yolo = YOLO()
#pipeline = rs.pipeline()
#config = rs.config()
#config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
#config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
# Start streaming
#pipeline.start(config)
capture = cv2.VideoCapture(1)
capture.set(3, 1280)
capture.set(4, 720)
time1 = time.perf_counter()
while 1:
if isEnd:
#pipeline.stop()
break
if isCameraOpen:
#frames = pipeline.wait_for_frames()
#depth_frame = frames.get_depth_frame()
#color_frame = frames.get_color_frame()
#if not depth_frame or not color_frame:
# continue
## Convert images to numpy arrays
#depth_image = np.asanyarray(depth_frame.get_data())
#color_image = np.asanyarray(color_frame.get_data())
## Apply colormap on depth image (image must be converted to 8-bit per pixel first)
#depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
ref, color_image = capture.read()
imageRGB = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)
im = Image.fromarray(imageRGB)
img_pix = im.toqpixmap().scaled(1280 * 0.7, 720 * 0.7)
ui.labelimg1.setPixmap(img_pix)
# color_image1 center is drawed in black
pic = cv2.imread('ratio62.jpg')
color_image1 = np.array(color_image * pic, dtype=np.uint8)
# color_image2 = copy.deepcopy(color_image)
color_image2 = imutils.resize(color_image1, width=600)
mask = self.fgbg.apply(color_image2)
# apply a series of erosions and dilations to eliminate noise
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# if the width and height are empty, grab the spatial dimensions
if self.W is None or self.H is None:
(self.H, self.W) = mask.shape[:2]
# compute the percentage of the mask that is "foreground"
p = (cv2.countNonZero(mask) / float(self.W * self.H)) * 100
# if there is less than No of the frame as "foreground"then we
# know that the motion has stopped and thus we should grab the frame
# Shake proof
######################################
if isDetect:
#results = []
# stop detect if time2-time1>19s
time2 = time.perf_counter()
if time2 - time1 > 19:
openAndCloseDetect()
frame = Image.fromarray(np.uint8(imageRGB))
bboxes = yolo.get_bbox(frame)
#print("bbox:")
#print(bboxes)
#print()
if bboxes == [None]:
image = np.asarray(frame)
else:
sort_boxes = []
for box in bboxes:
sort_box = SortBox(box, yolo.class_names)
sort_boxes.append(sort_box)
frame = np.asarray(frame)
image, obs = sort_and_draw_csv(image=frame,
boxes=sort_boxes,
labels=yolo.class_names,
obj_thresh=0.5,
mot_tracker=mot_tracker)
if results == []:
results = copy.deepcopy(obs)
#print("results1",results)
self.textSignal.emit(1)
else:
for ob in obs[::-1]:
if ob[0] > results[0][0]:
results.insert(0, ob)
#print(results)
self.textSignal.emit(2)
im = Image.fromarray(image)
img_pix = im.toqpixmap().scaled(1280 * 0.7, 720 * 0.7)
ui.labelimg2.setPixmap(img_pix)
else:
self.textSignal.emit(3)
if Turn:
if p < self.min_per and self.kinetic and self.frames_n > self.warmup:
# silent
self.kinetic_count += 1
if self.kinetic_count > self.max_count:
self.kinetic = False
self.kinetic_count = 0
print("Silent now")
ui.pushButton3.setText("当前状态:待转动")
# Kinetic
elif p > self.max_per and not self.kinetic:
self.kinetic_count += 1
if self.kinetic_count > self.max_count:
self.kinetic = True
self.kinetic_count = 0
print("Kinetic now")
ui.pushButton3.setText("当前状态:转动中")
ui.textEdit.append("转动中")
results.clear()
# display the frame and detect if there is a key press
cv2.imshow("Frame", color_image1)
cv2.imshow("Mask", mask)
cv2.waitKey(1)
self.frames_n += 1
#############################################
else:
ui.labelimg1.setText("摄像机已关闭")
if i[1] in display.keys():
display[i[1]] = int(display[i[1]]) + 1
else:
display[i[1]] = 1
print(display)
fps = 0.0
for i in range(nb_frames):
t1 = time.time()
_, frame = capture.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(np.uint8(frame))
bboxes = yolo.get_bbox(frame)
if bboxes == [None]:
image = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR)
else:
sort_boxes = []
for box in bboxes:
sort_box = SortBox(box, yolo.class_names)
sort_boxes.append(sort_box)
frame = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR)
image, obs = sort_and_draw_csv(image=frame,
boxes=sort_boxes,
labels=yolo.class_names,
obj_thresh=0.5,
