Upload speed prediction File

vehicle_counting_tensorflow-master/utils/speed_and_direction_prediction_module/speed_prediction.py

+from utils.image_utils import image_saver
+
+is_vehicle_detected = [0]
+current_frame_number_list = [0]
+current_frame_number_list_2 = [0]
+bottom_position_of_detected_vehicle = [0]
+
+
+def predict_speed(
+        top,
+        bottom,
+        right,
+        left,
+        current_frame_number,
+        crop_img,
+        roi_position,
+):
+    speed = 'n.a.'  # means not available, it is just initialization
+    direction = 'n.a.'  # means not available, it is just initialization
+    scale_constant = 1  # manual scaling because we did not performed camera calibration
+    isInROI = True  # is the object that is inside Region Of Interest
+    update_csv = False
+
+    if bottom < 250:
+        scale_constant = 1  # scale_constant is used for manual scaling because we did not performed camera calibration
+    elif bottom > 250 and bottom < 320:
+        scale_constant = 2  # scale_constant is used for manual scaling because we did not performed camera calibration
+    else:
+        isInROI = False
+
+    if len(bottom_position_of_detected_vehicle) != 0 and bottom \
+            - bottom_position_of_detected_vehicle[0] > 0 and 195 \
+            < bottom_position_of_detected_vehicle[0] \
+            and bottom_position_of_detected_vehicle[0] < 240 \
+            and roi_position < bottom + 100 and (current_frame_number - current_frame_number_list_2[0]) > 24:
+        is_vehicle_detected.insert(0, 1)
+        update_csv = True
+        image_saver.save_image(crop_img)  # save detected vehicle image
+        current_frame_number_list_2.insert(0, current_frame_number)
+    # for debugging
+    # print("bottom_position_of_detected_vehicle[0]: " + str(bottom_position_of_detected_vehicle[0]))
+    # print("bottom: " + str(bottom))
+    if bottom > bottom_position_of_detected_vehicle[0]:
+        direction = 'down'
+    else:
+        direction = 'up'
+
+    if isInROI:
+        pixel_length = bottom - bottom_position_of_detected_vehicle[0]
+        scale_real_length = pixel_length * 100
+        total_time_passed = current_frame_number - current_frame_number_list[0]
+        scale_real_time_passed = total_time_passed * 24  # get the elapsed total time for a vehicle to pass through ROI area (24 = fps)
+        if scale_real_time_passed != 0:
+            speed = scale_real_length / scale_real_time_passed / scale_constant  # performing manual scaling because we have not performed camera calibration
+            speed = speed / 6 * 40  # use reference constant to get vehicle speed prediction in kilometer unit
+            current_frame_number_list.insert(0, current_frame_number)
+            bottom_position_of_detected_vehicle.insert(0, bottom)
+    return (direction, speed, is_vehicle_detected, update_csv)