Migrating the EfficientSAM model to the OpenCV model zoo (#258)

* a

* add efficientsam model and basic demo

* update license

* remove example images

* update readme

* update readme

* update demo

* update demo

* update readme

* update SAM and __init__

* update demo and sam

* update label

* add present gif

* update readme

* add efficientSAM gif to readme of opencvzoo

* cv version 4.10.0， remove camera branch

README.md

@@ -73,6 +73,10 @@ Some examples are listed below. You can find more in the directory of each model
 
 ![messi](./models/human_segmentation_pphumanseg/example_outputs/messi.jpg)
 
+### Image Segmentation with [EfficientSAM](./models/image_segmentation_efficientsam/)
+
+![sam_present](./models/image_segmentation_efficientsam/example_outputs/sam_present.gif)
+
 ### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/)
 
 ![license plate detection](./models/license_plate_detection_yunet/example_outputs/lpd_yunet_demo.gif)

models/__init__.py

@@ -20,6 +20,7 @@ from .object_detection_yolox.yolox import YoloX
 from .facial_expression_recognition.facial_fer_model import FacialExpressionRecog
 from .object_tracking_vittrack.vittrack import VitTrack
 from .text_detection_ppocr.ppocr_det import PPOCRDet
+from .image_segmentation_efficientsam.efficientSAM import EfficientSAM
 
 class ModuleRegistery:
     def __init__(self, name):
@@ -94,3 +95,4 @@ MODELS.register(YoloX)
 MODELS.register(FacialExpressionRecog)
 MODELS.register(VitTrack)
 MODELS.register(PPOCRDet)
+MODELS.register(EfficientSAM)

models/image_segmentation_efficientsam/LICENSE

@@ -0,0 +1,201 @@
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models/image_segmentation_efficientsam/README.md

@@ -0,0 +1,44 @@
+# image_segmentation_efficientsam
+
+EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
+
+Notes:
+- The current implementation of the EfficientSAM demo uses the EfficientSAM-Ti model, which is specifically tailored for scenarios requiring higher speed and lightweight. 
+- MD5 value of "efficient_sam_vitt.pt" is 7A804DA508F30EFC59EC06711C8DCD62
+- SHA-256 value of "efficient_sam_vitt.pt" is DFF858B19600A46461CBB7DE98F796B23A7A888D9F5E34C0B033F7D6EB9E4E6A
+
+
+## Demo
+
+### Python
+Run the following command to try the demo:
+
+```shell
+python demo.py --input /path/to/image
+```
+
+Click only **once** on the object you wish to segment in the displayed image. After the click, the segmentation result will be shown in a new window.
+
+## Result
+
+Here are some of the sample results that were observed using the model:
+
+![test1_res.jpg](./example_outputs/example1.png)
+![test2_res.jpg](./example_outputs/example2.png)
+
+Video inference result:
+
+![sam_present.gif](./example_outputs/sam_present.gif)
+
+## Model metrics:
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+#### Contributor Details
+
+## Reference
+
+- https://arxiv.org/abs/2312.00863
+- https://github.com/yformer/EfficientSAM

models/image_segmentation_efficientsam/demo.py

@@ -0,0 +1,136 @@
+import argparse
+import numpy as np
+import cv2 as cv
+from efficientSAM import EfficientSAM
+
+# Check OpenCV version
+assert cv.__version__ >= "4.10.0", \
+       "Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
+
+# Valid combinations of backends and targets
+backend_target_pairs = [
+    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
+    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
+    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
+]
+
+parser = argparse.ArgumentParser(description='EfficientSAM Demo')
+parser.add_argument('--input', '-i', type=str,
+                    help='Set input path to a certain image.')
+parser.add_argument('--model', '-m', type=str, default='image_segmentation_efficientsam_ti_2024may.onnx',
+                    help='Set model path, defaults to image_segmentation_efficientsam_ti_2024may.onnx.')
+parser.add_argument('--backend_target', '-bt', type=int, default=0,
+                    help='''Choose one of the backend-target pair to run this demo:
+                        {:d}: (default) OpenCV implementation + CPU,
+                        {:d}: CUDA + GPU (CUDA),
+                        {:d}: CUDA + GPU (CUDA FP16),
+                        {:d}: TIM-VX + NPU,
+                        {:d}: CANN + NPU
+                    '''.format(*[x for x in range(len(backend_target_pairs))]))
+parser.add_argument('--save', '-s', action='store_true',
+                    help='Specify to save a file with results. Invalid in case of camera input.')
+args = parser.parse_args()
+
+#global click listener
+clicked_left = False
+#global point record in the window
+point = []
+
+def visualize(image, result):
+    """
+    Visualize the inference result on the input image.
+
+    Args:
+        image (np.ndarray): The input image.
+        result (np.ndarray): The inference result.
+
+    Returns:
+        vis_result (np.ndarray): The visualized result.
+    """
+    # get image and mask
+    vis_result = np.copy(image)
+    mask = np.copy(result)
+    # change mask to binary image
+    t, binary = cv.threshold(mask, 127, 255, cv.THRESH_BINARY)
+    assert set(np.unique(binary)) <= {0, 255}, "The mask must be a binary image"
+    # enhance red channel to make the segmentation more obviously
+    enhancement_factor = 1.8
+    red_channel = vis_result[:, :, 2]  
+    # update the channel
+    red_channel = np.where(binary == 255, np.minimum(red_channel * enhancement_factor, 255), red_channel)
+    vis_result[:, :, 2] = red_channel  
+    
+    # draw borders
+    contours, hierarchy = cv.findContours(binary, cv.RETR_LIST, cv.CHAIN_APPROX_TC89_L1)
+    cv.drawContours(vis_result, contours, contourIdx = -1, color = (255,255,255), thickness=2)
+    return vis_result
+
+def select(event, x, y, flags, param):
+    global clicked_left
+    # When the left mouse button is pressed, record the coordinates of the point where it is pressed
+    if event == cv.EVENT_LBUTTONUP:
+        point.append([x,y])
+        print("point:",point[0])
+        clicked_left = True
+
+if __name__ == '__main__':
+    backend_id = backend_target_pairs[args.backend_target][0]
+    target_id = backend_target_pairs[args.backend_target][1]
+    # Load the EfficientSAM model
+    model = EfficientSAM(modelPath=args.model)
+
+    if args.input is not None:
+        # Read image
+        image = cv.imread(args.input)
+        if image is None:
+            print('Could not open or find the image:', args.input)
+            exit(0)
+        # create window
+        image_window = "image: click on the thing whick you want to segment!"
+        cv.namedWindow(image_window, cv.WINDOW_NORMAL)
+        # change window size
+        cv.resizeWindow(image_window, 800 if image.shape[0] > 800 else image.shape[0], 600 if image.shape[1] > 600 else image.shape[1])
+        # put the window on the left of the screen
+        cv.moveWindow(image_window, 50, 100)
+        # set listener to record user's click point
+        cv.setMouseCallback(image_window, select)
+        # tips in the terminal
+        print("click the picture on the LEFT and see the result on the RIGHT!")
+        # show image
+        cv.imshow(image_window, image)
+        # waiting for click
+        while cv.waitKey(1) == -1 or clicked_left:
+            # receive click
+            if clicked_left:
+                # put the click point (x,y) into the model to predict
+                result = model.infer(image=image, points=point, labels=[1])
+                # get the visualized result
+                vis_result = visualize(image, result)
+                # create window to show visualized result
+                cv.namedWindow("vis_result", cv.WINDOW_NORMAL)
+                cv.resizeWindow("vis_result", 800 if vis_result.shape[0] > 800 else vis_result.shape[0], 600 if vis_result.shape[1] > 600 else vis_result.shape[1])
+                cv.moveWindow("vis_result", 851, 100)
+                cv.imshow("vis_result", vis_result)
+                # set click false to listen another click
+                clicked_left = False
+            elif cv.getWindowProperty(image_window, cv.WND_PROP_VISIBLE) < 1: 
+                # if click × to close the image window then ending
+                break
+            else:
+                # when not clicked, set point to empty
+                point = []
+        cv.destroyAllWindows()
+        
+        # Save results if save is true
+        if args.save:
+            cv.imwrite('./example_outputs/vis_result.jpg', vis_result)
+            cv.imwrite("./example_outputs/mask.jpg", result)
+            print('vis_result.jpg and mask.jpg are saved to ./example_outputs/')
+
+        
+    else:
+        print('Set input path to a certain image.')
+        pass
+        

models/image_segmentation_efficientsam/efficientSAM.py

@@ -0,0 +1,73 @@
+import numpy as np
+import cv2 as cv
+
+class EfficientSAM:
+    def __init__(self, modelPath, backendId=0, targetId=0):
+        self._modelPath = modelPath
+        self._backendId = backendId
+        self._targetId = targetId
+
+        self._model = cv.dnn.readNet(self._modelPath)
+        self._model.setPreferableBackend(self._backendId)
+        self._model.setPreferableTarget(self._targetId)
+        # 3 inputs
+        self._inputNames = ["batched_images", "batched_point_coords", "batched_point_labels"]  
+        
+        self._outputNames = ['output_masks']  # actual output layer name
+        self._currentInputSize = None
+        self._inputSize = [640, 640]  # input size for the model
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model.setPreferableBackend(self._backendId)
+        self._model.setPreferableTarget(self._targetId)
+
+    def _preprocess(self, image, points, labels):
+        
+        image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+        # record the input image size, (width, height)
+        self._currentInputSize = (image.shape[1], image.shape[0])
+        
+        image = cv.resize(image, self._inputSize)
+        
+        image = image.astype(np.float32, copy=False) / 255.0
+        
+        # convert points to (640*640) size space
+        for p in points:
+            p[0] = int(p[0] * self._inputSize[0]/self._currentInputSize[0])
+            p[1] = int(p[1]* self._inputSize[1]/self._currentInputSize[1])
+             
+        image_blob = cv.dnn.blobFromImage(image)
+        
+        points_blob = np.array([[points]], dtype=np.float32)
+        
+        labels_blob = np.array([[[labels]]])
+        
+        return image_blob, points_blob, labels_blob
+
+    def infer(self, image, points, labels):
+        # Preprocess
+        imageBlob, pointsBlob, labelsBlob = self._preprocess(image, points, labels)
+        # Forward
+        self._model.setInput(imageBlob, self._inputNames[0])
+        self._model.setInput(pointsBlob, self._inputNames[1])
+        self._model.setInput(labelsBlob, self._inputNames[2])
+        outputBlob = self._model.forward()
+        # Postprocess
+        results = self._postprocess(outputBlob)
+
+        return results
+
+    def _postprocess(self, outputBlob):
+        mask = outputBlob[0, 0, 0, :, :] >= 0
+        
+        mask_uint8 = (mask * 255).astype(np.uint8)
+        # change to real image size
+        mask_uint8 = cv.resize(mask_uint8, dsize=self._currentInputSize, interpolation=2)
+                
+        return mask_uint8