def detect():
data = request.get_json()
img_path = data['path']
try:
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print("img", img_path, img.shape)
target_boxes = detector.detect_bbox(copy.deepcopy(img))
all_points = npPointsCraft.detect(img, target_boxes)
all_points = [ps for ps in all_points if len(ps)]
# cut zones
rgb_zones = [
getCvZoneRGB(img, reshapePoints(rect, 1)) for rect in all_points
]
zones = convertCvZonesRGBtoBGR(rgb_zones)
# find standart
region_ids, count_lines = optionsDetector.predict(zones)
region_names = optionsDetector.getRegionLabels(region_ids)
# find text with postprocessing by standart
text_arr = textDetector.predict(zones, region_names, count_lines)
return ujson.dumps(dict(res=text_arr, img_path=img_path))
except Exception as e:
exc_type, exc_value, exc_tb = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_tb)
return ujson.dumps(dict(error=str(e), img_path=img_path))
def test(dir_name, fname):
start_time = time.time()
img_path = os.path.join(dir_name, fname)
with open(img_path, 'rb') as in_file:
img = jpeg.decode(in_file.read())
image_load_time = time.time() - start_time
start_time = time.time()
targetBoxes = detector.detect_bbox(img)
detect_bbox_time = time.time() - start_time
start_time = time.time()
all_points = npPointsCraft.detect(img, targetBoxes)
all_points = [ps for ps in all_points if len(ps)]
craft_time = time.time() - start_time
start_time = time.time()
zones = convertCvZonesRGBtoBGR(
[getCvZoneRGB(img, reshapePoints(rect, 1)) for rect in all_points])
perspective_align_time = time.time() - start_time
start_time = time.time()
regionIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
classification_time = time.time() - start_time
start_time = time.time()
_ = textDetector.predict(zones, regionNames, countLines)
ocr_time = time.time() - start_time
return image_load_time, detect_bbox_time, craft_time, perspective_align_time, classification_time, ocr_time
async def test(dirName, fname, y, min_bbox_acc = 0.5, verbose=0):
nGood = 0
nBad = 0
img_path = os.path.join(dirName, fname)
if verbose==1:
print(colored(f"__________ \t\t {img_path} \t\t __________", "blue"))
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
targetBoxes = detector.detect_bbox(img)
all_points = npPointsCraft.detect(img, targetBoxes,[5,2,0])
# for images/14.jpeg bug
all_points = [ps for ps in all_points if len(ps)]
print('ll_points')
print(all_points)
# cut zones
toShowZones = [getCvZoneRGB(img, reshapePoints(rect,1)) for rect in all_points]
#toShowZones = getCvZonesRGB(img, all_points)
zones = convertCvZonesRGBtoBGR(toShowZones)
#zones = rectDetector.get_cv_zonesBGR(img, all_points)
#toShowZones = rectDetector.get_cv_zonesRGB(img, all_points)
for zone, points in zip(toShowZones, all_points):
plt.axis("off")
plt.imshow(zone)
plt.show()
# find standart
regionIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
print(regionNames)
print(countLines)
# find text with postprocessing by standart
textArr = textDetector.predict(zones, regionNames, countLines)
print(textArr)
# draw rect and 4 points
for targetBox, points in zip(targetBoxes, all_points):
# draw
cv2.rectangle(img,
(int(targetBox[0]), int(targetBox[1])),
(int(targetBox[2]), int(targetBox[3])),
(0,120,255),
3)
#print(points, points.shape)
cv2.polylines(img, np.array([points], np.int32), True, (255,120,255),3)
plt.imshow(img)
plt.show()
for yText in y:
if yText in textArr:
print(colored(f"OK: TEXT:{yText} \t\t\t RESULTS:{textArr} \n\t\t\t\t\t in PATH:{img_path}", 'green'))
nGood += 1
else:
print(colored(f"NOT OK: TEXT:{yText} \t\t\t RESULTS:{textArr} \n\t\t\t\t\t in PATH:{img_path} ", 'red'))
nBad += 1
return nGood, nBad
def read_number_plates(img):
targetBoxes = detector.detect_bbox(img)
all_points = npPointsCraft.detect(img, targetBoxes, [5, 2, 0])
# cut zones
zones = convertCvZonesRGBtoBGR(
[getCvZoneRGB(img, reshapePoints(rect, 1)) for rect in all_points])
# predict zones attributes
regionIds, stateIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
# find text with postprocessing by standart
textArr = textDetector.predict(zones)
textArr = textPostprocessing(textArr, regionNames)
return textArr, regionNames
})
rootDir = '../images/*'
imgs = [mpimg.imread(img_path) for img_path in glob.glob(rootDir)]
for img in imgs:
targetBoxes = detector.detect_bbox(copy.deepcopy(img))
targetBoxes = targetBoxes
all_points = npPointsCraft.detect(img, targetBoxes)
all_points = [ps for ps in all_points if len(ps)]
print(all_points)
# cut zones
toShowZones = [getCvZoneRGB(img, reshapePoints(rect, 1)) for rect in all_points]
zones = convertCvZonesRGBtoBGR(toShowZones)
for zone, points in zip(toShowZones, all_points):
plt.axis("off")
plt.imshow(zone)
plt.show()
# find standart
regionIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
print(regionNames)
print(countLines)
# find text with postprocessing by standart
textArr = textDetector.predict(zones, regionNames, countLines)
print(textArr)
from NomeroffNet.OptionsDetector import OptionsDetector
from NomeroffNet.TextDetector import TextDetector
from NomeroffNet.TextPostprocessing import textPostprocessing
# load models
optionsDetector = OptionsDetector()
optionsDetector.load("latest")
textDetector = TextDetector.get_static_module("eu")()
textDetector.load("latest")
# Detect numberplate
img_path = 'images/example2.jpeg'
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
targetBoxes = detector.detect_bbox(img)
all_points = npPointsCraft.detect(img, targetBoxes,[5,2,0])
# cut zones
zones = convertCvZonesRGBtoBGR([getCvZoneRGB(img, reshapePoints(rect, 1)) for rect in all_points])
# predict zones attributes
regionIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
# find text with postprocessing by standart
textArr = textDetector.predict(zones)
textArr = textPostprocessing(textArr, regionNames)
print(textArr)