models & requirements

.gitignore

@@ -0,0 +1 @@
+.idea

app.py

@@ -1,9 +1,19 @@
 import gradio as gr
+import cv2
+from detector.utils import detect_plates, detect_chars
 
 
-def greet(name):
-    return "Hello " + name + "!!"
+def predict(img):
+    plates = detect_plates(img)
+    if len(plates) > 0:
+        for plate in plates:
+            p1, p2, crop = plate
+            if len(crop) > 0:
+                cv2.rectangle(img, p1, p2, (0, 0, 255), 2)
+                text, crop = detect_chars(crop)
+                cv2.putText(img, text, p1, cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 255, 0), 5)
+    return img
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface = gr.Interface(fn=predict, inputs="image", outputs="image")
 iface.launch()

detector/static/chars.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:216d9926ea5ac6195b420b98b668fe8a404f180753e83032904bbcf90906b76d
+size 14516597

detector/static/plates.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34eb52d76e7dcf3e5a5d855ddb8ca95d88633c45bfa08f52e0985e2735e9754c
+size 173134301

detector/utils.py

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+import torch
+import numpy as np
+import cv2
+import os
+
+BASE_DIR = os.path.abspath(os.getcwd())
+
+model_plates = torch.hub.load('ultralytics/yolov5', 'custom',
+                              path=os.path.join(BASE_DIR, 'detector', 'static', 'plates.pt'))
+# model.conf = 0.60  # NMS confidence threshold
+# model.iou = 0.60  # NMS IoU threshold
+# model.agnostic = False  # NMS class-agnostic
+# model.multi_label = False  # NMS multiple labels per box
+# model.classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+# model.max_det = 1  # maximum number of detections per image
+# model.amp = False  # Automatic Mixed Precision (AMP) inference
+
+model_chars = torch.hub.load('ultralytics/yolov5', 'custom',
+                             path=os.path.join(BASE_DIR, 'detector', 'static', 'chars.pt'))
+
+
+def pad_img_to_fit_bbox(img, x1, x2, y1, y2):
+    img = np.pad(img, ((np.abs(np.minimum(0, y1)), np.maximum(y2 - img.shape[0], 0)),
+                       (np.abs(np.minimum(0, x1)), np.maximum(x2 - img.shape[1], 0)), (0, 0)), mode="constant")
+    y1 += np.abs(np.minimum(0, y1))
+    y2 += np.abs(np.minimum(0, y1))
+    x1 += np.abs(np.minimum(0, x1))
+    x2 += np.abs(np.minimum(0, x1))
+    return img, x1, x2, y1, y2
+
+
+def imcrop(img, bbox):
+    x1, y1, x2, y2 = bbox
+    if x1 < 0 or y1 < 0 or x2 > img.shape[1] or y2 > img.shape[0]:
+        img, x1, x2, y1, y2 = pad_img_to_fit_bbox(img, x1, x2, y1, y2)
+    return img[y1:y2, x1:x2, :]
+
+
+def detect_plates(img):
+    detect = model_plates(img)
+    records = detect.pandas().xyxy[0].to_dict(orient='records')
+    plates = []
+    if records:
+        for plate in records:
+            xi, yi, xf, yf = int(plate['xmin']), int(plate['ymin']), int(plate['xmax']), int(plate['ymax'])
+            crop = imcrop(img, (xi, yi, xf, yf))
+            plates.append(((xi, yi), (xf, yf), crop))
+    return plates
+
+
+def detect_chars(img):
+    img = cv2.resize(img, (640, 480))
+    detect = model_chars(img)
+    records = detect.pandas().xyxy[0].to_dict(orient='records')
+    yolo = np.squeeze(detect.render())
+    text = ''
+    if records:
+        records = sorted(records, key=lambda d: d['xmin'])
+        text = ''.join([i.get('name') for i in records])
+    return text, img
+
+
+def save_plates(img):
+    detect = model_plates(img)
+    detect.crop(save=True)
+
+
+# def yolo_detections_to_norfair_detections(yolo_detections, track_points="centroid"):
+#     """convert detections_as_xywh to norfair detections"""
+#     norfair_detections = []
+#
+#     if track_points == "centroid":
+#         detections_as_xywh = yolo_detections.xywh[0]
+#         for detection_as_xywh in detections_as_xywh:
+#             centroid = np.array(
+#                 [detection_as_xywh[0].item(), detection_as_xywh[1].item()]
+#             )
+#             scores = np.array([detection_as_xywh[4].item()])
+#             norfair_detections.append(
+#                 Detection(
+#                     points=centroid,
+#                     scores=scores,
+#                     label=int(detection_as_xywh[-1].item()),
+#                 )
+#             )
+#     elif track_points == "bbox":
+#         detections_as_xyxy = yolo_detections.xyxy[0]
+#         for detection_as_xyxy in detections_as_xyxy:
+#             bbox = np.array(
+#                 [
+#                     [detection_as_xyxy[0].item(), detection_as_xyxy[1].item()],
+#                     [detection_as_xyxy[2].item(), detection_as_xyxy[3].item()],
+#                 ]
+#             )
+#             scores = np.array(
+#                 [detection_as_xyxy[4].item(), detection_as_xyxy[4].item()]
+#             )
+#             norfair_detections.append(
+#                 Detection(
+#                     points=bbox, scores=scores, label=int(detection_as_xyxy[-1].item())
+#                 )
+#             )
+#
+#     return norfair_detections

requirements.txt

@@ -0,0 +1,127 @@
+absl-py==1.3.0
+aiohttp==3.8.3
+aiosignal==1.3.1
+anyio==3.6.2
+appnope==0.1.3
+asttokens==2.2.0
+async-timeout==4.0.2
+attrs==22.1.0
+backcall==0.2.0
+bcrypt==4.0.1
+cachetools==5.2.0
+certifi==2021.5.30
+cffi==1.15.1
+chardet==4.0.0
+charset-normalizer==2.1.1
+click==8.1.3
+commonmark==0.9.1
+contourpy==1.0.6
+cryptography==38.0.4
+cycler==0.11.0
+decorator==5.1.1
+easyocr==1.6.2
+executing==1.2.0
+fastapi==0.88.0
+ffmpy==0.3.0
+filterpy==1.4.5
+fonttools==4.38.0
+frozenlist==1.3.3
+fsspec==2022.11.0
+gitdb==4.0.10
+GitPython==3.1.29
+glob2==0.7
+google-auth==2.15.0
+google-auth-oauthlib==0.4.6
+gradio==3.12.0
+grpcio==1.51.1
+h11==0.12.0
+httpcore==0.15.0
+httpx==0.23.1
+idna==3.4
+imageio==2.22.4
+importlib-metadata==5.1.0
+imutils==0.5.4
+ipython==8.7.0
+jedi==0.18.2
+Jinja2==3.1.2
+kiwisolver==1.4.4
+linkify-it-py==1.0.3
+Markdown==3.4.1
+markdown-it-py==2.1.0
+MarkupSafe==2.1.1
+matplotlib==3.6.2
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+multidict==6.0.3
+networkx==2.8.8
+ninja==1.11.1
+norfair==2.1.1
+numpy==1.23.5
+oauthlib==3.2.2
+opencv-python==4.6.0.66
+opencv-python-headless==4.5.4.60
+orjson==3.8.3
+packaging==21.3
+pandas==1.5.2
+paramiko==2.12.0
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.3.0
+prompt-toolkit==3.0.33
+protobuf==3.20.3
+psutil==5.9.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pyclipper==1.3.0.post4
+pycparser==2.21
+pycryptodome==3.16.0
+pydantic==1.10.2
+pydub==0.25.1
+Pygments==2.13.0
+PyNaCl==1.5.0
+pyparsing==3.0.9
+python-bidi==0.4.2
+python-dateutil==2.8.2
+python-dotenv==0.21.0
+python-multipart==0.0.5
+pytz==2022.6
+PyWavelets==1.4.1
+PyYAML==6.0
+requests==2.28.1
+requests-oauthlib==1.3.1
+requests-toolbelt==0.10.1
+rfc3986==1.5.0
+rich==12.6.0
+rsa==4.9
+scikit-image==0.19.3
+scipy==1.9.3
+seaborn==0.12.1
+Shapely==1.8.5.post1
+six==1.16.0
+smmap==5.0.0
+sniffio==1.3.0
+stack-data==0.6.2
+starlette==0.22.0
+tensorboard==2.11.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+thop==0.1.1.post2209072238
+tifffile==2022.10.10
+torch==1.13.0
+torchvision==0.14.0
+tqdm==4.64.1
+traitlets==5.6.0
+typing_extensions==4.4.0
+uc-micro-py==1.0.1
+urllib3==1.26.13
+uvicorn==0.20.0
+wcwidth==0.2.5
+websockets==10.4
+Werkzeug==2.2.2
+wget==3.2
+yarl==1.8.2
+zipp==3.11.0