rm models

app.py

@@ -72,14 +72,12 @@ with gr.Blocks() as demo:
         with gr.Row():
             image_input = gr.Image(
                 type='filepath',
-                label="Input Image",
-                source="upload"
+                label="Input Image"
             )
 
             image_output = gr.Image(
                 type='pil',
-                label="Output Image",
-                source="canvas"
+                label="Output Image"
             )
 
         text_button = gr.Button("Detect")

insectid/detector.py

@@ -1,8 +1,6 @@
 import os
-
 import khandy
 import numpy as np
-
 from .base import OnnxModel
 from .base import check_image_dtype_and_shape
 
@@ -10,15 +8,16 @@ from .base import check_image_dtype_and_shape
 class InsectDetector(OnnxModel):
     def __init__(self):
         current_dir = os.path.dirname(os.path.abspath(__file__))
-        model_path = os.path.join(current_dir, 'models/quarrying_insect_detector.onnx')
+        model_path = os.path.join(
+            current_dir, './models/quarrying_insect_detector.onnx')
         self.input_width = 640
         self.input_height = 640
         super(InsectDetector, self).__init__(model_path)
 
     def _preprocess(self, image):
         check_image_dtype_and_shape(image)
-        
-         # image size normalization
+
+        # image size normalization
         image, scale, pad_left, pad_top = khandy.letterbox_image(
             image, self.input_width, self.input_height, 0, return_scale=True)
         # image channel normalization
@@ -26,17 +25,18 @@ class InsectDetector(OnnxModel):
         # image dtype normalization
         image = khandy.rescale_image(image, 'auto', np.float32)
         # to tensor
-        image = np.transpose(image, (2,0,1))
+        image = np.transpose(image, (2, 0, 1))
         image = np.expand_dims(image, axis=0)
         return image, scale, pad_left, pad_top
-        
+
     def _post_process(self, outputs_list, scale, pad_left, pad_top, conf_thresh, iou_thresh):
         pred = outputs_list[0][0]
         pass_t = pred[:, 4] > conf_thresh
         pred = pred[pass_t]
-        
+
         boxes = khandy.convert_boxes_format(pred[:, :4], 'cxcywh', 'xyxy')
-        boxes = khandy.unletterbox_2d_points(boxes, scale, pad_left, pad_top, False)
+        boxes = khandy.unletterbox_2d_points(
+            boxes, scale, pad_left, pad_top, False)
         confs = np.max(pred[:, 5:] * pred[:, 4:5], axis=-1)
         classes = np.argmax(pred[:, 5:] * pred[:, 4:5], axis=-1)
         keep = khandy.non_max_suppression(boxes, confs, iou_thresh)
@@ -46,11 +46,10 @@ class InsectDetector(OnnxModel):
         image, scale, pad_left, pad_top = self._preprocess(image)
         outputs_list = self.forward(image)
         boxes, confs, classes = self._post_process(
-            outputs_list, 
-            scale=scale, 
+            outputs_list,
+            scale=scale,
             pad_left=pad_left,
             pad_top=pad_top,
-            conf_thresh=conf_thresh, 
+            conf_thresh=conf_thresh,
             iou_thresh=iou_thresh)
         return boxes, confs, classes
-        

insectid/identifier.py

@@ -1,23 +1,24 @@
 import os
 import copy
-from collections import OrderedDict
-
 import khandy
 import numpy as np
-
 from .base import OnnxModel
+from collections import OrderedDict
 from .base import check_image_dtype_and_shape
 
 
 class InsectIdentifier(OnnxModel):
     def __init__(self):
         current_dir = os.path.dirname(os.path.abspath(__file__))
-        model_path = os.path.join(current_dir, 'models/quarrying_insect_identifier.onnx')
-        label_map_path = os.path.join(current_dir, 'models/quarrying_insectid_label_map.txt')
+        model_path = os.path.join(
+            current_dir, './models/quarrying_insect_identifier.onnx')
+        label_map_path = os.path.join(
+            current_dir, './models/quarrying_insectid_label_map.txt')
         super(InsectIdentifier, self).__init__(model_path)
-        
+
         self.label_name_dict = self._get_label_name_dict(label_map_path)
-        self.names = [self.label_name_dict[i]['chinese_name'] for i in range(len(self.label_name_dict))]
+        self.names = [self.label_name_dict[i]['chinese_name']
+                      for i in range(len(self.label_name_dict))]
         self.num_classes = len(self.label_name_dict)
 
     @staticmethod
@@ -26,14 +27,14 @@ class InsectIdentifier(OnnxModel):
         label_name_dict = {}
         for record in records:
             label, chinese_name, latin_name = record.split(',')
-            label_name_dict[int(label)] = OrderedDict([('chinese_name', chinese_name), 
+            label_name_dict[int(label)] = OrderedDict([('chinese_name', chinese_name),
                                                        ('latin_name', latin_name)])
         return label_name_dict
-        
+
     @staticmethod
     def _preprocess(image):
         check_image_dtype_and_shape(image)
-        
+
         # image size normalization
         image = khandy.letterbox_image(image, 224, 224)
         # image channel normalization
@@ -43,21 +44,21 @@ class InsectIdentifier(OnnxModel):
         mean, stddev = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
         image = khandy.normalize_image_value(image, mean, stddev, 'auto')
         # to tensor
-        image = np.transpose(image, (2,0,1))
+        image = np.transpose(image, (2, 0, 1))
         image = np.expand_dims(image, axis=0)
         return image
-        
+
     def predict(self, image):
         inputs = self._preprocess(image)
         logits = self.forward(inputs)
         probs = khandy.softmax(logits)
         return probs
-        
+
     def identify(self, image, topk=5):
         assert isinstance(topk, int)
         if topk <= 0 or topk > self.num_classes:
             topk = self.num_classes
-            
+
         probs = self.predict(image)
         topk_probs, topk_indices = khandy.top_k(probs, topk)
 
@@ -67,5 +68,3 @@ class InsectIdentifier(OnnxModel):
             one_result['probability'] = prob
             results.append(one_result)
         return results
-        
-        

insectid/models/quarrying_insect_detector.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3d1c0615c8dc604248d1b8c48c8414a7b7a84d653ce54ba612ff928ba8f38745
-size 28315428

insectid/models/quarrying_insect_identifier.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3e9c9c498633cbb7797560009e1826b34a6ed2aa5b95c9e7b1e184ad8cbb2355
-size 22675272