def _scan_file_image(self):
"""Load and process (scan for barcodes) an image from file
"""
filepath = str(QtGui.QFileDialog.getOpenFileName(self, "Open file"))
if filepath:
cv_image = Image.from_file(filepath)
gray_image = cv_image.to_grayscale()
# Scan the image for barcodes
plate_type = self._config.plate_type.value()
barcode_size = self._config.barcode_size.value()
SlotScanner.DEBUG = self._config.slot_images.value()
SlotScanner.DEBUG_DIR = self._config.slot_image_directory.value()
if plate_type == "None":
scanner = OpenScanner(barcode_size)
else:
scanner = GeometryScanner(plate_type, barcode_size)
scan_result = scanner.scan_next_frame(gray_image, is_single_image=True)
plate = scan_result.plate()
# If the scan was successful, store the results
if plate is not None:
self.recordTable.add_record(plate, cv_image)
else:
error = "There was a problem scanning the image:\n{}".format(scan_result.error())
QtGui.QMessageBox.warning(self, "Scanning Error", error)
def _scan_file_image(self):
"""Load and process (scan for barcodes) an image from file
"""
filepath = str(QtGui.QFileDialog.getOpenFileName(self, 'Open file'))
if filepath:
cv_image = Image.from_file(filepath)
gray_image = cv_image.to_grayscale()
# Scan the image for barcodes
plate_type = self._config.plate_type.value()
barcode_size = self._config.barcode_size.value()
SlotScanner.DEBUG = self._config.slot_images.value()
SlotScanner.DEBUG_DIR = self._config.slot_image_directory.value()
if plate_type == "None":
scanner = OpenScanner(barcode_size)
else:
scanner = GeometryScanner(plate_type, barcode_size)
scan_result = scanner.scan_next_frame(gray_image, is_single_image=True)
plate = scan_result.plate()
# If the scan was successful, store the results
if plate is not None:
self.recordTable.add_record(plate, cv_image)
else:
error = "There was a problem scanning the image:\n{}".format(scan_result.error())
QtGui.QMessageBox.warning(self, "Scanning Error", error)
def run_scans(img_file, expected_codes):
filepath = os.path.join(TEST_IMG_DIR, img_file)
cv_image = Image.from_file(filepath)
gray_image = cv_image.to_grayscale()
results = GeometryScanner("Unipuck",
[14]).scan_next_frame(gray_image,
is_single_image=True)
plate = results.plate()
store_scan(img_file, plate, cv_image)
correctly_read_count = 0
slots = [plate.slot(i) for i in range(16)]
num_found = len([s for s in slots if s.state() == s.VALID])
#barcodes_for_debug = [s.barcode_data() for s in slots]
assert num_found == len(expected_codes)
for expected_code in expected_codes:
expected_code_text = expected_code[0]
slot = expected_code[1]
barcode_read = plate.slot(slot).barcode_data()
#print(barcode_read, expected_code_text)
if barcode_read == expected_code_text:
correctly_read_count += 1
assert correctly_read_count == len(expected_codes)
def run_scans(img_file, expected_codes):
filepath = os.path.join(TEST_IMG_DIR, img_file)
print(img_file)
cv_image = Image.from_file(filepath)
gray_image = cv_image.to_grayscale()
results = GeometryScanner("Unipuck", [14]).scan_next_frame(gray_image, is_single_image=True)
plate = results.plate()
if plate != None:
for expected_code in expected_codes:
expected_code_text = expected_code[0] #text
slot_number = expected_code[1] #number
slot = plate.slot(slot_number)
read_code_text = slot.barcode_data()
#print(slot_number, read_code_text, expected_code_text)
if slot.state() == slot.VALID:
assert read_code_text == expected_code_text
def run_tests():
# Run all of the test cases
total = 0
correct = 0
found = 0
start = time.clock()
for case in TEST_CASES:
file = case[0]
expected_codes = case[1]
total += len(expected_codes)
filename = TEST_IMG_DIR + file
cv_image = Image.from_file(filename)
gray_image = cv_image.to_grayscale()
results = GeometryScanner("Unipuck",
14).scan_next_frame(gray_image,
is_single_image=True)
plate = results.plate()
store_scan(plate, cv_image)
pass_count = 0
slots = [plate.slot(i) for i in range(16)]
num_found = len([s for s in slots if s.state() == s.VALID])
for expected_code in expected_codes:
text = expected_code[0]
slot = expected_code[1]
data = plate.slot(slot).barcode_data()
if data == text:
pass_count += 1
result = "pass" if pass_count == len(expected_codes) else "FAIL"
print("{0} - {1} - {2}/{3} matches ({4} found)".format(
file, result, pass_count, len(expected_codes), num_found))
correct += pass_count
found += num_found
end = time.clock()
print("Summary | {0} secs | {1} correct | {2} found | {3} total".format(
end - start, correct, found, total))
def run_tests():
# Run all of the test cases
total = 0
correct = 0
found = 0
start = time.clock()
for case in TEST_CASES:
file = case[0]
expected_codes = case[1]
total += len(expected_codes)
filename = TEST_IMG_DIR + file
cv_image = Image.from_file(filename)
gray_image = cv_image.to_grayscale()
results = GeometryScanner("Unipuck", 14).scan_next_frame(gray_image, is_single_image=True)
plate = results.plate()
store_scan(plate, cv_image)
pass_count = 0
slots = [plate.slot(i) for i in range(16)]
num_found = len([s for s in slots if s.state() == s.VALID])
for expected_code in expected_codes:
text = expected_code[0]
slot = expected_code[1]
data = plate.slot(slot).barcode_data()
if data == text:
pass_count += 1
result = "pass" if pass_count == len(expected_codes) else "FAIL"
print("{0} - {1} - {2}/{3} matches ({4} found)".format(file, result, pass_count, len(expected_codes), num_found))
correct += pass_count
found += num_found
end = time.clock()
print("Summary | {0} secs | {1} correct | {2} found | {3} total".format(end-start, correct,found,total))
def get_holder_image(self):
image = Image.from_file(self.holder_image_path)
return image
def get_image(self):
image = Image.from_file(self.image_path)
return image
""" This file gives an illustrated demonstration of process involved in locating a datamatrix in an image. This isn't to be used as a test as it re-implements some parts of the locator algorithm """ import cv2 from dls_util.image import Image, Color from dls_util.shape import Point from dls_barcode.datamatrix.locate.locate_contour import ContourLocator as CL # Load the image file folder = "./demo-resources/" file = folder + "dm.png" image_original = Image.from_file(file) # Convert to a grayscale image image_mono = image_original.to_grayscale() # Perform adaptive threshold block_size = 35 C = 16 image_threshold = CL._do_threshold(image_mono, block_size, C) # Perform morphological close close_size = 2 image_morphed = CL._do_close_morph(image_threshold, close_size) # Find a bunch of contours in the image. contours = CL._get_contours(image_morphed)
def image(self):
image = Image.from_file(self.image_path)
return image
