Update app.py

app.py

@@ -1,77 +1,77 @@
-import os
-import yolov5
-
-# load model
-model = yolov5.load('keremberke/yolov5m-license-plate')
-
-# set model parameters
-model.conf = 0.5  # NMS confidence threshold
-model.iou = 0.25  # NMS IoU threshold
-model.agnostic = False  # NMS class-agnostic
-model.multi_label = False  # NMS multiple labels per box
-model.max_det = 1000  # maximum number of detections per image
-
-# set image
-def license_plate_detect(img):
-    # perform inference
-    results = model(img, size=640)
+# import os
+# import yolov5
+
+# # load model
+# model = yolov5.load('keremberke/yolov5m-license-plate')
+
+# # set model parameters
+# model.conf = 0.5  # NMS confidence threshold
+# model.iou = 0.25  # NMS IoU threshold
+# model.agnostic = False  # NMS class-agnostic
+# model.multi_label = False  # NMS multiple labels per box
+# model.max_det = 1000  # maximum number of detections per image
+
+# # set image
+# def license_plate_detect(img):
+#     # perform inference
+#     results = model(img, size=640)
     
-    # inference with test time augmentation
-    results = model(img, augment=True)
+#     # inference with test time augmentation
+#     results = model(img, augment=True)
     
-    # parse results
-    if len(results.pred):
-        predictions = results.pred[0]
-        boxes = predictions[:, :4] # x1, y1, x2, y2
-        scores = predictions[:, 4]
-        categories = predictions[:, 5]
-        return boxes
-
-from PIL import Image
-# image = Image.open(img)
-import pytesseract
-
-def read_license_number(img):
-    boxes = license_plate_detect(img)
-    if boxes:
-        return [pytesseract.image_to_string(
-                    image.crop(bbox.tolist()))
-               for bbox in boxes]
-
-from transformers import CLIPProcessor, CLIPModel
-vit_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-def zero_shot_classification(image, labels):
-    inputs = processor(text=labels,
-                       images=image,
-                       return_tensors="pt",
-                       padding=True)
-    outputs = vit_model(**inputs)
-    logits_per_image = outputs.logits_per_image # this is the image-text similarity score
-    return logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
-
-installed_list = []
-# image = Image.open(requests.get(url, stream=True).raw)
-def check_solarplant_installed_by_license(license_number_list):
-    if len(installed_list):
-        return [license_number in installed_list
-                for license_number in license_number_list]
-
-def check_solarplant_installed_by_image(image, output_label=False):
-    zero_shot_class_labels = ["bus with solar panel grids",
-                              "bus without solar panel grids"]
-    probs = zero_shot_classification(image, zero_shot_class_labels)
-    if output_label:
-        return zero_shot_class_labels[probs.argmax().item()]
-    return probs.argmax().item() == 0
-
-def check_solarplant_broken(image):
-    zero_shot_class_labels = ["white broken solar panel",
-                              "normal black solar panel grids"]
-    probs = zero_shot_classification(image, zero_shot_class_labels)
-    idx = probs.argmax().item()
-    return zero_shot_class_labels[idx].split(" ")[1-idx]
+#     # parse results
+#     if len(results.pred):
+#         predictions = results.pred[0]
+#         boxes = predictions[:, :4] # x1, y1, x2, y2
+#         scores = predictions[:, 4]
+#         categories = predictions[:, 5]
+#         return boxes
+
+# from PIL import Image
+# # image = Image.open(img)
+# import pytesseract
+
+# def read_license_number(img):
+#     boxes = license_plate_detect(img)
+#     if boxes:
+#         return [pytesseract.image_to_string(
+#                     image.crop(bbox.tolist()))
+#                for bbox in boxes]
+
+# from transformers import CLIPProcessor, CLIPModel
+# vit_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+# processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# def zero_shot_classification(image, labels):
+#     inputs = processor(text=labels,
+#                        images=image,
+#                        return_tensors="pt",
+#                        padding=True)
+#     outputs = vit_model(**inputs)
+#     logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+#     return logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+
+# installed_list = []
+# # image = Image.open(requests.get(url, stream=True).raw)
+# def check_solarplant_installed_by_license(license_number_list):
+#     if len(installed_list):
+#         return [license_number in installed_list
+#                 for license_number in license_number_list]
+
+# def check_solarplant_installed_by_image(image, output_label=False):
+#     zero_shot_class_labels = ["bus with solar panel grids",
+#                               "bus without solar panel grids"]
+#     probs = zero_shot_classification(image, zero_shot_class_labels)
+#     if output_label:
+#         return zero_shot_class_labels[probs.argmax().item()]
+#     return probs.argmax().item() == 0
+
+# def check_solarplant_broken(image):
+#     zero_shot_class_labels = ["white broken solar panel",
+#                               "normal black solar panel grids"]
+#     probs = zero_shot_classification(image, zero_shot_class_labels)
+#     idx = probs.argmax().item()
+#     return zero_shot_class_labels[idx].split(" ")[1-idx]
 
 from fastsam import FastSAM, FastSAMPrompt
 os.system('wget https://huggingface.co/spaces/An-619/FastSAM/resolve/main/weights/FastSAM.pt')
@@ -109,10 +109,11 @@ def greet(img):
     lns = read_license_number(img)
     if len(lns):
         seg = segment_solar_panel(img)
-        return (seg,
-                "車牌： " + '; '.join(lns) + "\n\n" \
-                + "類型： "+ check_solarplant_installed_by_image(img, True) + "\n\n" \
-                + "狀態：" + check_solarplant_broken(img))
+        return (seg, '')
+        # return (seg,
+        #         "車牌： " + '; '.join(lns) + "\n\n" \
+        #         + "類型： "+ check_solarplant_installed_by_image(img, True) + "\n\n" \
+        #         + "狀態：" + check_solarplant_broken(img))
     return (img, "空地。。。")
 
 iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"])