def predict(self, payload):
# download image
img_url = payload["url"]
image = preprocess_utils.get_url_image(img_url)
# detect the bounding boxes
boxes = utils.get_yolo_boxes(
self.yolov3_model,
image,
self.net_h,
self.net_w,
self.anchors,
self.obj_thresh,
self.nms_thresh,
len(self.labels),
tensorflow_model=False,
)
# purge bounding boxes with a low confidence score
aux = []
for b in boxes:
label = -1
for i in range(len(b.classes)):
if b.classes[i] > self.box_confidence_score:
label = i
if label >= 0:
aux.append(b)
boxes = aux
del aux
# if bounding boxes have been detected
dec_words = []
if len(boxes) > 0:
# create set of images of the detected license plates
lps = []
for b in boxes:
lp = image[b.ymin : b.ymax, b.xmin : b.xmax]
lps.append(lp)
# run batch inference
try:
prediction_groups = self.recognition_model_pipeline.recognize(lps)
except ValueError:
# exception can occur when the images are too small
prediction_groups = []
# process pipeline output
image_list = []
for img_predictions in prediction_groups:
boxes_per_image = []
for predictions in img_predictions:
boxes_per_image.append([predictions[0], predictions[1].tolist()])
image_list.append(boxes_per_image)
# reorder text within detected LPs based on horizontal position
dec_lps = preprocess_utils.reorder_recognized_words(image_list)
for dec_lp in dec_lps:
dec_words.append([word[0] for word in dec_lp])
# if there are no recognized LPs, then don't draw them
if len(dec_words) == 0:
dec_words = [[] for i in range(len(boxes))]
# draw predictions as overlays on the source image
draw_image = bbox_utils.draw_boxes(
image,
boxes,
overlay_text=dec_words,
labels=["LP"],
obj_thresh=self.box_confidence_score,
)
# image represented in bytes
byte_im = preprocess_utils.image_to_jpeg_bytes(draw_image)
# encode image
image_enc = base64.b64encode(byte_im).decode("utf-8")
# image with draw boxes overlayed
return image_enc
def main(img_url_src, yolov3_endpoint, crnn_endpoint, output):
# get the image in bytes representation
image = preprocess_utils.get_url_image(img_url_src)
image_bytes = preprocess_utils.image_to_jpeg_bytes(image)
# encode image
image_enc = base64.b64encode(image_bytes).decode("utf-8")
image_dump = json.dumps({"img": image_enc})
# make yolov3 api request
resp = requests.post(yolov3_endpoint,
data=image_dump,
headers={"content-type": "application/json"})
# parse response
boxes_raw = resp.json()["boxes"]
boxes = []
for b in boxes_raw:
box = bbox_utils.BoundBox(*b)
boxes.append(box)
# purge bounding boxes with a low confidence score
confidence_score = 0.8
aux = []
for b in boxes:
label = -1
for i in range(len(b.classes)):
if b.classes[i] > confidence_score:
label = i
if label >= 0:
aux.append(b)
boxes = aux
del aux
dec_words = []
if len(boxes) > 0:
# create set of images of the detected license plates
lps = []
for b in boxes:
lp = image[b.ymin:b.ymax, b.xmin:b.xmax]
jpeg = preprocess_utils.image_to_jpeg_nparray(lp)
lps.append(jpeg)
# encode the cropped license plates
lps = pickle.dumps(lps, protocol=0)
lps_enc = base64.b64encode(lps).decode("utf-8")
lps_dump = json.dumps({"imgs": lps_enc})
# make crnn api request
resp = requests.post(crnn_endpoint,
data=lps_dump,
headers={"content-type": "application/json"})
# parse the response
dec_lps = resp.json()["license-plates"]
dec_lps = preprocess_utils.reorder_recognized_words(dec_lps)
for dec_lp in dec_lps:
dec_words.append([word[0] for word in dec_lp])
if len(dec_words) == 0:
dec_words = [[] for i in range(len(boxes))]
# draw predictions as overlays on the source image
draw_image = bbox_utils.draw_boxes(image,
boxes,
overlay_text=dec_words,
labels=["LP"],
obj_thresh=confidence_score)
# and save it to disk
cv2.imwrite(output, draw_image)