def __init__(self,
markerOrders=[7, 8],
defaultKernelSize=21,
scalingParameter=2500):
# Initialize camera driver.
# Open output window.
cv.NamedWindow('filterdemo', cv.CV_WINDOW_AUTOSIZE)
self.setFocus()
# Select the camera where the images should be grabbed from.
self.camera = cv.CaptureFromCAM(0)
#self.setResolution()
# Storage for image processing.
self.currentFrame = None
self.processedFrame = None
self.running = True
# Storage for trackers.
self.trackers = []
self.oldLocations = []
# Initialize trackers.
for markerOrder in markerOrders:
temp = ImageAnalyzer(downscaleFactor=1)
temp.addMarkerToTrack(markerOrder, defaultKernelSize,
scalingParameter)
self.trackers.append(temp)
self.oldLocations.append(MarkerPose(None, None, None, None))
self.cnt = 0
self.defaultOrientation = 0
def display_image(self):
indexes = self.table_view_sm.selectedIndexes()
index = indexes[0]
file_name = str(index.data())
if (file_name.endswith("fits") or file_name.endswith("fit")):
file_with_path = os.path.join(self.current_directory.cur_dir_path,
file_name)
self.im_disp = ImageAnalyzer(file_with_path, parent=self)
def __init__(self, store_pics=False):
self.cam = picamera.PiCamera()
self.cam.resolution = (resolution['x'], resolution['y'])
self.analyzer = ImageAnalyzer(adaptive_threshold=True)
self.logger = Log.instance('TargetDetection')
self.store_pics = store_pics
if os.path.exists(dir_name):
shutil.rmtree(dir_name)
os.makedirs(dir_name)
def estimate_distances(pics_dir):
analyzer = ImageAnalyzer()
for (path, _, files) in os.walk(pics_dir):
for f in sorted(files):
pic = os.path.join(path, f)
img = cv2.imread(pic)
try:
px, mm = analyzer.calculate_distance(img)
logger.debug('distance: {:4d}px\t{:3.0f}mm'.format(px, mm))
except Exception as e:
logger.error(e)
def main():
print("***** Assignment 2 Initiated")
analyzer = ImageAnalyzer(DATA_DIR, DEPS_DIR, OUTPUT_DIR)
# ------------------------------------------------------------------------------------------------------------------
# --------------------------- SET PATH AND NAMES FOR ALL IMAGE TYPES
# ------------------------------------------------------------------------------------------------------------------
print("***** Importing and Undistorting/Demoscaicing Raw Images")
image_names = []
# image_names.append(['mono_left', 'mono_left'])
# image_names.append(['mono_rear', 'mono_rear'])
# image_names.append(['mono_right', 'mono_right'])
# image_names.append(['stereo/centre', 'stereo'])
image_names.append(['stereo/left', 'stereo'])
# image_names.append(['stereo/right', 'stereo'])
for name in image_names:
analyzer.undistortImages(name[0], name[1])
# ------------------------------------------------------------------------------------------------------------------
# --------------------------- SAVE IMAGES
# ------------------------------------------------------------------------------------------------------------------
print("***** Saving Undistorted/Demoscaiced Images")
# analyzer.saveImages()
# ------------------------------------------------------------------------------------------------------------------
# --------------------------- GENERATE 3D POINT CLOUDS FOR ENTIRE TIME PERIOD
# ------------------------------------------------------------------------------------------------------------------
print("***** Generating 3D Point Cloud")
ptcloud_names = []
ptcloud_names.append(['lms_front', 'gps/ins.csv'])
# ptcloud_names.append(['lms_rear', 'gps/ins.csv'])
# ptcloud_names.append(['ldmrs', 'gps/ins.csv'])
# for name in ptcloud_names:
# analyzer.buildLidarPointCloud(name[0], name[1])
# ------------------------------------------------------------------------------------------------------------------
# --------------------------- SAVE 3D POINT CLOUDS
# ------------------------------------------------------------------------------------------------------------------
print("***** Saving 3D Point Clouds")
analyzer.saveRawLidarPointClouds()
# ------------------------------------------------------------------------------------------------------------------
# --------------------------- PROJECT POINT CLOUDS ONTO IMAGES AND SAVE FIGURE
# ------------------------------------------------------------------------------------------------------------------
print("***** Projecting Point Clouds On Images")
for cloud_type in ptcloud_names:
for img_type in image_names:
analyzer.showPointCloudsOnImages(img_type[0], cloud_type[0], cloud_type[1])
print("***** Assignment 2 Complete")
def process(pics_dir, adaptive_threshold):
analyzer = ImageAnalyzer(adaptive_threshold=adaptive_threshold)
timings = []
for (path, _, files) in os.walk(pics_dir):
for f in sorted(files):
pic = os.path.join(path, f)
img = cv2.imread(pic)
try:
start = time.process_time()
px, mm = analyzer.calculate_distance(img)
end = time.process_time()
timings.append(int((end - start) * 1000))
logger.debug('distance: {:4d}px\t{:3.0f}mm'.format(px, mm))
except Exception as e:
logger.error(e)
return timings
def __init__(self,
markerOrders=[7, 8],
defaultKernelSize=21,
scalingParameter=2500,
cameraDevice=0):
# Initialize camera driver.
# Open output window.
# cv.NamedWindow('filterdemo', cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow('filterdemo', cv.CV_WINDOW_NORMAL)
# cv.NamedWindow('filterdemo', cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow('temp_kernel', cv.CV_WINDOW_NORMAL)
cv.NamedWindow('small_image', cv.CV_WINDOW_NORMAL)
self.cameraDevice = cameraDevice
# If an interface has been given to the constructor, it's a camdevice and we need to set some properties. If it's not, it means it's a file.
if (isinstance(cameraDevice, numbers.Integral)):
self.setFocus()
# Select the camera where the images should be grabbed from.
self.camera = cv.CaptureFromCAM(cameraDevice)
else:
self.camera = cv.CaptureFromFile(cameraDevice)
self.setResolution()
# Storage for image processing.
self.currentFrame = None
self.processedFrame = None
self.running = True
# Storage for trackers.
self.trackers = []
self.windowedTrackers = []
self.oldLocations = []
# Initialize trackers.
for markerOrder in markerOrders:
temp = ImageAnalyzer(downscaleFactor=1)
temp.addMarkerToTrack(markerOrder, defaultKernelSize,
scalingParameter)
self.trackers.append(temp)
self.windowedTrackers.append(
TrackerInWindowMode(markerOrder, defaultKernelSize))
self.oldLocations.append(MarkerPose(None, None, None, None))
self.cnt = 0
self.defaultOrientation = 0
import os
from ImageAnalyzer import ImageAnalyzer
from plate_recognition_service.PlateRecognitionService import PlateRecognitionService
if __name__ == "__main__":
character_recognition_model_path = f'{os.getcwd()}/models/svc/svc.pkl'
image_analyzer = ImageAnalyzer(character_recognition_model_path,
0.4,
reset_between_predictions=True)
service = PlateRecognitionService(image_analyzer).start(port=5000)
ir_images_directory = scripts_directory.replace("/scripts", "/images/ir_images")
#ir_images_directory = "/Users/stoudenmiersh/OneDrive/DCIM/IR_Photos"
reports_directory = scripts_directory.replace("/scripts", "/reports")
outfile = open(reports_directory + "/ir_images.csv", 'w')
# looping through every image in the folder containing the images
for root, dirs, files in os.walk(ir_images_directory):
if(len(dirs) == 0):
temp_path = root
for root2, dirs2, files2 in os.walk(temp_path):
for f in files2:
#counter+=1
if (f[-3:].lower() == "jpg"):
#if (f[-3:].lower() == "jpg" and counter % 10 == 0): # only detects the file if it is a .jpg
temp_analyzer = ImageAnalyzer(root2+"/"+f)
x_values.append(counter)
image_width = temp_analyzer.getImageWidth()
image_height = temp_analyzer.getImageHeight()
temp_analyzer.cropImage(int((7/16.0)*image_width), 0,
int((9/16.0)*image_width), int((1/8.0)*image_height))
y_values.append(temp_analyzer.getImageHls()[1])
outfile.write(str(counter) + "," + str(temp_analyzer.getImageHls()[1]) + "\n")
counter+=1
print(str(counter) + " " + root2+"/"+f + " "+str(temp_analyzer.getImageHls()[1]))
#plt.plot(x_values,y_values)
# Use polyfit.
#best_fit = polyfit(x_values, y_values, 3)
#polynomial = poly1d(best_fit)
from ImageAnalyzer import ImageAnalyzer
imgAnalyzer = ImageAnalyzer('sample.png')
for c in imgAnalyzer.contourize():
imgAnalyzer.analyzefigure(c)
