def pipeline(frame):
global ct, history, ROI_MASK, MASK_VALID, GRID_MATRIX_INIT, grid_matrix, frame_seq
timestamp_area = frame[t_i_1_s:t_i_1_e,t_i_2_s:t_i_2_e]
# cv2.imshow("cropped", timestamp_area)
# cv2.waitKey(0)
gray = cv2.cvtColor(timestamp_area, cv2.COLOR_BGR2GRAY)
#gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
#gray = cv2.medianBlur(gray, 3)
text = pytesseract.image_to_string(gray)
print(text)
if MASK_VALID == 0:
ROI_MASK = np.zeros_like(frame[:,:,0], dtype=np.uint8)
np_corners = np.array(g_corners, dtype = np.int32)
cv2.fillPoly(ROI_MASK, [np_corners], 255)
MASK_VALID = 1
if GRID_MATRIX_INIT == 0:
grid_matrix = np.zeros((int(grid_step) + 1, int(grid_step) + 1))
GRID_MATRIX_INIT = 1
#print(ROI_MASK.shape)
fm = Frame(frame, g_corners, ROI_MASK, grid_matrix, grid_step, frame_seq)
mpimg.imsave("current_frame.jpg", frame)
results = detect(net, meta, b"current_frame.jpg")
#print(fm.img.shape)
for detection in results:
ob = Object(detection, frame_seq)
fm.add_object(ob)
# update frame number
frame_seq += 1
if frame_seq > FRAME_SQE_MAX:
frame_seq = 0
fm.draw_boxes()
fm.add_labels()
fm.draw_grid()
fm.color_grid()
fm.color_NROI()
# cv2.line(fm.box_img, (0,420), (700,420), (255,0,0))
# cv2.line(fm.box_img, (0,380), (700,380), (255,0,0))
fm.detect_crossing(ct)
if len(history) > 0 :
detect_crossing_with_history(fm, history.pop())
#print(len(fm.objects))
fm.plot_measurements()
fm.draw_trajectory()
history.append(fm)
return fm.box_img
