def test_get_target_gps_location(self):
self.test_drone_gps_location = Location(0,0,100)
self.test_image_midpoint = (100,100)
self.test_target_midpoint = (50,50)
self.difference_vector = ((self.test_target_midpoint[0] - self.test_image_midpoint[0]) / math.sqrt(GCSSettings.IMAGE_PROC_PPSI) / 12, (self.test_target_midpoint[1] - self.test_image_midpoint[1]) / math.sqrt(GCSSettings.IMAGE_PROC_PPSI) / 12, 0)
self.assertEqual(round(inverse_haversine(self.test_drone_gps_location, self.difference_vector).get_lat()), round(get_target_gps_location(self.test_image_midpoint, self.test_target_midpoint, self.test_drone_gps_location).get_lat()))
self.test_drone_gps_location = Location(1000,1000,5)
self.test_image_midpoint = (40,75)
self.test_target_midpoint = (1000,510)
self.difference_vector = ((self.test_target_midpoint[0] - self.test_image_midpoint[0]) / math.sqrt(GCSSettings.IMAGE_PROC_PPSI) / 12, (self.test_target_midpoint[1] - self.test_image_midpoint[1]) / math.sqrt(GCSSettings.IMAGE_PROC_PPSI) / 12, 0)
self.assertEqual(round(inverse_haversine(self.test_drone_gps_location, self.difference_vector).get_lat()), round(get_target_gps_location(self.test_image_midpoint, self.test_target_midpoint, self.test_drone_gps_location).get_lat()))
def get_location(vehicle):
"""
Convert the vehicle's current location to a Location object
:param vehicle: The vehicle to convert
"""
latitude = vehicle.location.global_relative_frame.lat
longitude = vehicle.location.global_relative_frame.lon
altitude = vehicle.location.global_relative_frame.alt * 3.28084
return Location(latitude, longitude, altitude)
def get_obstacle_location(obstacle, MSL_ALT):
"""
Get an Obstacle's location
:param obstacle: The obstacle to convert
:type obstacle: StationaryObstacle or MovingObstacle
"""
latitude = obstacle.latitude
longitude = obstacle.longitude
if isinstance(obstacle, interop.StationaryObstacle):
altitude = obstacle.cylinder_height
else:
altitude = obstacle.altitude_msl + obstacle.sphere_radius - MSL_ALT
return Location(latitude, longitude, altitude)
def inverse_haversine(location1, point):
"""
Calculate a second GPS point through using a single GPS point and a
different in XY units
:param location1: The first GPS coordinate
:type location1: Location
:param point: The point in the map that the obstacle occupies
:type point: Numpy Array
"""
dy = point[1] / (3.28084 * 1000)
dx = point[0] / (3.28084 * 1000)
lat = location1.get_lat() + (dy / 111.195)
lon = location1.get_lon() + (
dx / (math.cos(math.radians(
(lat + location1.get_lat()) / 2.0)) * 111.191))
alt = point[2] / 3.28084
return Location(lat, lon, alt)
def autonomous_image_processing():
TARGET_MAP_PATH = "../../scripts/gcs/static/auto_imgs/"
AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH = "../../scripts/gcs/static/auto_crops/"
SAVE_PATH_FOR_POSTING = "static/auto_crops/"
processed_target_maps = []
while True:
sleep(1)
try:
map_list_response = requests.get("http://localhost:5000/get/imgs")
target_map_list = map_list_response.json()
needs_refresh = False
except:
continue
interop_response = requests.get("http://localhost:5000/get/interop")
interop_json = interop_response.json()
fly_zones = construct_fly_zone_polygon_from_json(interop_json)
runway_zone, runway_grass1_zone, runway_grass2_zone, runway_grass3_zone, runway_grass4_zone, runway_grass5_zone = construct_runway_fly_zone():
location_log_response = requests.get("http://localhost:5000/get/uav_location_log")
location_log = location_log_response.json()["location_log"]
for map_index in range(len(target_map_list)):
if target_map_list[str(map_index)] in processed_target_maps:
if map_index + 1 == len(target_map_list):
needs_refresh = True
continue
else:
current_target_map_name = target_map_list[str(map_index)]
processed_target_maps.append(current_target_map_name)
break
if len(target_map_list) == 0 or needs_refresh:
continue
current_target_map_path = os.path.join(TARGET_MAP_PATH, current_target_map_name)
combo_target_detection_result_list = SingleTargetMapDetector.detect_single_target_map(current_target_map_path)
single_target_crops = combo_target_detection_result_list[0]
json_file = combo_target_detection_result_list[1]
image = Image.open(current_target_map_path)
json_file["target_map_center_location"] = (image.width / 2, image.height / 2)
json_file["target_map_timestamp"] = get_image_timestamp_from_metadata(current_target_map_path)
print("Identifying targets within %s" % current_target_map_name)
for index_in_single_target_crops in range(len(single_target_crops)):
json_file["image_processing_results"][index_in_single_target_crops]["target_index"] = index_in_single_target_crops + 1
current_crop_path = os.path.join(AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH, current_target_map_name + " - " + str(index_in_single_target_crops + 1) + ".png")
single_target_crops[index_in_single_target_crops].save(current_crop_path)
# adds target location
target_map_center_pixel_coordinates = json_file["target_map_center_location"]
target_pixel_coordinates = json_file["image_processing_results"][index_in_single_target_crops]["target_location"]
target_time = json_file["target_map_timestamp"]
closest_time_index = 0
least_time_difference = location_log[0]["epoch_time"]
for index in range(len(location_log)):
difference_in_times = abs(target_time - location_log[closest_time_index]["epoch_time"])
if difference_in_times <= least_time_difference:
closest_time_index = index
least_time_difference = difference_in_times
drone_gps_location = Location(location_log[closest_time_index]["latitude"], location_log[closest_time_index]["longitude"], location_log[closest_time_index]["altitude"])
target_location = get_target_gps_location(target_map_center_pixel_coordinates, target_pixel_coordinates, drone_gps_location)
# check if target is in fly zones
if fly_zones.contains_point([target_location.get_lat(), target_location.get_lon()]) == 0:
print("target eleminated -- not in range")
continue
# check if target is in runway or runway grass. if on runway, eleminate, if on grass, do not eleminate
if runway_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
# target in runway zone -- check if on grass
if runway_grass1_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
pass
elif runway_grass2_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
pass
elif runway_grass3_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
pass
elif runway_grass4_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
pass
elif runway_grass5_zone.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
pass
else:
# target not in grass; must be on runway -- eleminate
continue
json_file["image_processing_results"][index_in_single_target_crops]["latitude"] = target_location.get_lat()
json_file["image_processing_results"][index_in_single_target_crops]["longitude"] = target_location.get_lon()
shape_type = ShapeDetection.ShapeClassificationTwo(current_crop_path).get_shape_type()
json_file["image_processing_results"][index_in_single_target_crops]["target_shape_type"] = shape_type
color_classifying_results = Classifiers.ColorClassifier(current_crop_path).get_color()
shape_color = color_classifying_results[0]
letter_color = color_classifying_results[1]
letter = "a"
orientation = random.choice(["n","ne","e","se","s","sw","w","nw"])
json_file["image_processing_results"][index_in_single_target_crops]["target_shape_color"] = shape_color
json_file["image_processing_results"][index_in_single_target_crops]["target_letter_color"] = letter_color
json_file["image_processing_results"][index_in_single_target_crops]["target_orientation"] = orientation
json_file["image_processing_results"][index_in_single_target_crops]["target_letter"] = letter
# create the specific target post
posting_json = {}
posting_json["target"] = []
posting_json["target"].append({
"target_shape_color": shape_color,
"target_letter_color": letter_color,
"target_shape_type": shape_type,
"target_orientation": orientation,
"target_letter": letter,
"latitude": target_location.get_lat(),
"longitude": target_location.get_lon(),
"current_crop_path": os.path.join(SAVE_PATH_FOR_POSTING, current_target_map_name + " - " + str(index_in_single_target_crops + 1) + ".png")
})
requests.post("http://localhost:5000/post/autonomous_img_proc_target", json=posting_json)
print("posted a %s %s" % (shape_color, shape_type))
with open(os.path.join(AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH, current_target_map_name[:-4] + ".json"), 'w') as fp:
json.dump(json_file, fp, indent=4)
def load_sd_card(location_log, interop_client_array):
SD_PATH = os.path.join("/Volumes", GCSSettings.SD_CARD_NAME, "DCIM")
while True:
print("Waiting SD Card to load...")
if os.path.exists(SD_PATH):
break
sleep(5)
print("SD Card loaded")
if os.path.exists("static/all_imgs"):
shutil.rmtree("static/all_imgs")
os.makedirs("static/all_imgs")
if os.path.exists("static/imgs"):
shutil.rmtree("static/imgs")
os.makedirs("static/imgs")
if os.path.exists("static/crops"):
shutil.rmtree("static/crops")
os.makedirs("static/crops")
if os.path.exists("static/autonomous_crops"):
shutil.rmtree("static/autonomous_crops")
os.makedirs("static/autonomous_crops")
fly_zones = construct_fly_zone_polygon(interop_client_array)
for pic_folder in os.listdir(SD_PATH):
if not "." in pic_folder:
pictures_dir_path = os.path.join(SD_PATH, pic_folder)
for pic_name in os.listdir(pictures_dir_path):
if ".jpg" in pic_name.lower() and pic_name[:1] != ".":
pic_path = os.path.join(pictures_dir_path, pic_name)
shutil.copy2(pic_path, "static/all_imgs")
target_time = get_image_timestamp_from_metadata(pic_path)
closest_time_index = 0
least_time_difference = location_log[0]["epoch_time"]
for index in range(len(location_log)):
difference_in_times = target_time - location_log[
closest_time_index]["epoch_time"]
if abs(difference_in_times) <= least_time_difference:
closest_time_index = index
least_time_difference = difference_in_times
drone_gps_location = Location(
location_log[closest_time_index]["latitude"],
location_log[closest_time_index]["longitude"],
location_log[closest_time_index]["altitude"])
if fly_zones.contains_point([
drone_gps_location.get_lat(),
drone_gps_location.get_lon()
]) == 0:
# not in range
continue
shutil.copy2(pic_path, "static/imgs")
def test_submit_position(self):
"""
Test POST of position data
"""
self.interop_client.post_telemetry(Location(38, 76, 100), 350.0)
def load_sd_card(location_log, interop_client_array):
SD_PATH = os.path.join("/Volumes", GCSSettings.SD_CARD_NAME, "DCIM")
print("Waiting SD Card to load...")
if os.path.exists("static/all_imgs"):
shutil.rmtree("static/all_imgs")
os.makedirs("static/all_imgs")
if os.path.exists("static/imgs"):
shutil.rmtree("static/imgs")
os.makedirs("static/imgs")
if os.path.exists("static/crops"):
shutil.rmtree("static/crops")
os.makedirs("static/crops")
if os.path.exists("static/auto_crops"):
shutil.rmtree("static/auto_crops")
os.makedirs("static/auto_crops")
if os.path.exists("static/auto_imgs"):
shutil.rmtree("static/auto_imgs")
os.makedirs("static/auto_imgs")
print("Cleared Image Processing folders")
while True:
if os.path.exists(SD_PATH):
break
sleep(5)
print("SD Card loaded")
fly_zones = construct_fly_zone_polygon(interop_client_array)
for pic_folder in os.listdir(SD_PATH):
if not "." in pic_folder:
pictures_dir_path = os.path.join(SD_PATH, pic_folder)
for pic_name in os.listdir(pictures_dir_path):
if ".jpg" in pic_name.lower() and pic_name[:1] != ".":
pic_path = os.path.join(pictures_dir_path, pic_name)
shutil.copy2(pic_path, "static/all_imgs")
target_time = get_image_timestamp_from_metadata(pic_path)
closest_time_index = 0
least_time_difference = location_log[0]["epoch_time"]
for index in range(len(location_log)):
difference_in_times = target_time - location_log[index]["epoch_time"]
if abs(difference_in_times) <= least_time_difference:
closest_time_index = index
least_time_difference = difference_in_times
drone_gps_location = Location(location_log[closest_time_index]["latitude"], location_log[closest_time_index]["longitude"], location_log[closest_time_index]["altitude"])
"""
if not fly_zones.contains_point([drone_gps_location.get_lat(), drone_gps_location.get_lon()]):
# not in designated flyzone from interop -- eleminate
print(pic_name)
print(drone_gps_location.get_lat())
print(drone_gps_location.get_lon())
print("is eleminated for not being in range")
continue
"""
if abs(drone_gps_location.get_alt() - GCSSettings.SEARCH_AREA_ALT) > 30:
# drone too low -- eliminate
print(pic_name + " is eleminated for being too low or too high at " + str(drone_gps_location.get_alt()) + " ft")
continue
shutil.copy2(pic_path, "static/auto_imgs")
shutil.copy2(pic_path, "static/imgs")
print(pic_name + " accepted at " + str(drone_gps_location.get_alt()) + " ft")
def get_location(self):
"""
Return the VehicleState object's location
"""
return Location(self.get_lat(), self.get_lon(), self.get_alt())
def autonomous_image_processing():
TARGET_MAP_PATH = "static/imgs/"
AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH = "static/autonomous_crops"
processed_target_maps = []
while True:
sleep(1)
try:
map_list_response = requests.get("http://localhost:5000/get/imgs")
target_map_list = map_list_response.json()
except:
continue
interop_response = requests.get("http://localhost:5000/get/interop")
interop_json = interop_response.json()
fly_zones = construct_fly_zone_polygon_from_json(interop_json)
location_log_response = requests.get(
"http://localhost:5000/get/uav_location_log")
location_log = location_log_response.json()["location_log"]
for map_index in range(len(target_map_list)):
if not target_map_list[str(map_index)] in processed_target_maps:
current_target_map_name = target_map_list[str(map_index)]
processed_target_maps.append(current_target_map_name)
break
current_target_map_path = os.path.join(TARGET_MAP_PATH,
current_target_map_name)
combo_target_detection_result_list = SingleTargetMapDetector.detect_single_target_map(
current_target_map_path)
single_target_crops = combo_target_detection_result_list[0]
json_file = combo_target_detection_result_list[1]
image = Image.open(current_target_map_path)
json_file["target_map_center_location"] = (image.width / 2,
image.height / 2)
json_file["target_map_timestamp"] = get_image_timestamp_from_metadata(
current_target_map_path)
for index_in_single_target_crops in range(len(single_target_crops)):
json_file["image_processing_results"][
index_in_single_target_crops][
"target_index"] = index_in_single_target_crops + 1
current_crop_path = os.path.join(
AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH,
current_target_map_name + " - " +
str(index_in_single_target_crops + 1) + ".png")
single_target_crops[index_in_single_target_crops].save(
current_crop_path)
# adds target location
target_map_center_pixel_coordinates = json_file[
"target_map_center_location"]
target_pixel_coordinates = json_file["image_processing_results"][
index_in_single_target_crops]["target_location"]
target_time = json_file["target_map_timestamp"]
closest_time_index = 0
least_time_difference = location_log[0]["epoch_time"]
for index in range(len(location_log)):
difference_in_times = abs(
target_time -
location_log[closest_time_index]["epoch_time"])
if difference_in_times <= least_time_difference:
closest_time_index = index
least_time_difference = difference_in_times
drone_gps_location = Location(
location_log[closest_time_index]["latitude"],
location_log[closest_time_index]["longitude"],
location_log[closest_time_index]["altitude"])
target_location = get_target_gps_location(
target_map_center_pixel_coordinates, target_pixel_coordinates,
drone_gps_location)
if fly_zones.contains_point(
[target_location.get_lat(),
target_location.get_lon()]) == 0:
# not in range
continue
json_file["image_processing_results"][
index_in_single_target_crops][
"latitude"] = target_location.get_lat()
json_file["image_processing_results"][
index_in_single_target_crops][
"longitude"] = target_location.get_lon()
shape_type = ShapeDetection.ShapeClassificationTwo(
current_crop_path).get_shape_type()
json_file["image_processing_results"][
index_in_single_target_crops]["target_shape_type"] = shape_type
color_classifying_results = Classifiers.ColorClassifier(
current_crop_path).get_color()
shape_color = color_classifying_results[0]
letter_color = color_classifying_results[1]
letter = "a"
orientation = random.choice(
["n", "ne", "e", "se", "s", "sw", "w", "nw"])
json_file["image_processing_results"][
index_in_single_target_crops][
"target_shape_color"] = shape_color
json_file["image_processing_results"][
index_in_single_target_crops][
"target_letter_color"] = letter_color
json_file["image_processing_results"][
index_in_single_target_crops][
"target_orientation"] = orientation
json_file["image_processing_results"][
index_in_single_target_crops]["target_letter"] = letter
# create the specific target post
posting_json = {}
posting_json["target"] = []
posting_json["target"].append({
"target_shape_color":
shape_color,
"target_letter_color":
letter_color,
"target_shape_type":
shape_type,
"target_orientation":
orientation,
"target_letter":
letter,
"latitude":
target_location.get_lat(),
"longitude":
target_location.get_lon(),
"current_crop_path":
current_crop_path
})
requests.post(
"http://localhost:5000/post/autonomous_img_proc_target",
json=posting_json)
with open(
os.path.join(AUTONOMOUS_IMAGE_PROCESSING_SAVE_PATH,
current_target_map_name[:-4] + ".json"),
'w') as fp:
json.dump(json_file, fp, indent=4)