Create app.py (#1)

- Create app.py (79f9dfbd1915681f3e6bf2cbf208b5f7a4bfb27b)
- Update app.py (aedefaa9c307a34f0e5a9113cae0d4526944beb6)
- Update app.py (8a88adaa6e09a3f137158c1b6ce54cf45ebf8d92)

app.py

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+import os
+os.system('wget https://huggingface.co/spaces/An-619/FastSAM/resolve/main/weights/FastSAM.pt')
+
+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)
+    
+    # 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
+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'
+def segment_solar_panel(img):
+    # os.system('python Inference.py --model_path FastSAM.pt --img_path bus.jpg  --text_prompt "solar panel grids"')
+    img = img.convert("RGB")
+
+    everything_results = model(img, device=DEVICE, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9,)
+    prompt_process = FastSAMPrompt(img, 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='./bus.jpg',)
+    return Image.Open('./bus.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()