Update app.py

app.py

@@ -39,3 +39,81 @@ def read_license_number(img):
         return [pytesseract.image_to_string(
                     image.crop(bbox.tolist()))
                for bbox in boxes]
+
+from transformers import CLIPProcessor, CLIPModel
+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 = 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][1-idx]
+
+from fastsam import FastSAM, FastSAMPrompt
+
+model = FastSAM('./FastSAM.pt')
+DEVICE = 'cpu'
+IMAGE_PATH = 'sam.jpg'
+def segment_solar_panel(img):
+    img.Save(IMAGE_PATH)
+    everything_results = model(IMAGE_PATH, device=DEVICE, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9,)
+    prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE)
+    
+    # everything prompt
+    ann = prompt_process.everything_prompt()
+    
+    # bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+    ann = prompt_process.box_prompt(bbox=[[200, 200, 300, 300]])
+    
+    # text prompt
+    ann = prompt_process.text_prompt(text='solar panel grids')
+    
+    # point prompt
+    # points default [[0,0]] [[x1,y1],[x2,y2]]
+    # point_label default [0] [1,0] 0:background, 1:foreground
+    ann = prompt_process.point_prompt(points=[[620, 360]], pointlabel=[1])
+    
+    prompt_process.plot(annotations=ann,output_path='./dog.jpg',)
+    return Image.Open('./dog.jpg')
+
+
+import gradio as gr
+
+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 (img, "空地。。。")
+
+iface = gr.Interface(fn=greet, inputs="image", outputs=["image", "text"])
+iface.launch()