model upload

app.py

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+import gradio as gr
+import onnxruntime as rt
+import cv2
+import numpy as np
+from PIL import Image
+
+H, W = 224, 224
+classes=['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable',
+         'dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
+
+providers = ['CPUExecutionProvider']
+
+m = rt.InferenceSession("./model/yolo_efficient.onnx", providers=providers)
+
+def nms(final_boxes, scores, IOU_threshold=0):
+    scores = np.array(scores)
+    final_boxes = np.array(final_boxes)
+
+    boxes = final_boxes[...,:-1]
+
+    boxes = [list(map(int, i)) for i in boxes]
+    boxes = np.array(boxes)
+    #print(boxes)
+    
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+
+    area = (x2 - x1)*(y2 - y1)
+
+    order = np.argsort(scores)
+    #print(order)
+
+    pick = []
+
+    while len(order) > 0:
+        last = len(order)-1
+        i = order[last]
+        pick.append(i)
+
+        suppress = [last]
+
+        if len(order)==0:
+            break
+
+        for pos in range(last):
+            j = order[pos]
+
+            xx1 = max(x1[i], x1[j])
+            yy1 = max(y1[i], y1[j])
+            xx2 = min(x2[i], x2[j])
+            yy2 = min(y2[i], y2[j])
+
+            w = max(0, xx2-xx1+1)
+            h = max(0, yy2-yy1+1)
+
+            overlap = float(w*h)/area[j]
+
+            if overlap > IOU_threshold:
+                suppress.append(pos)
+
+        order = np.delete(order, suppress)
+
+    return final_boxes[pick]
+
+def detect_obj(input_image):
+    try:
+        image = np.array(input_image)
+        image = cv2.resize(image, (H, W))
+        img = image
+
+        image = image.astype(np.float32)
+        image = np.expand_dims(image, axis=0)
+        print(image.shape)
+
+        output = m.run(['reshape'], {"input": image})
+        output = np.squeeze(output, axis=0)
+        print(output.shape)
+
+        THRESH=.25
+
+
+        object_positions = np.concatenate(
+                [np.stack(np.where(output[..., 0]>=THRESH), axis=-1),
+                 np.stack(np.where(output[..., 5]>=THRESH), axis=-1)], axis=0
+        )
+
+        selected_output = []
+        for indices in object_positions:
+                selected_output.append(output[indices[0]][indices[1]][indices[2]])
+        selected_output = np.array(selected_output)
+
+        final_boxes = []
+        final_scores = []
+
+        for i,pos in enumerate(object_positions):
+            for j in range(2):
+                if selected_output[i][j*5]>THRESH:
+                    output_box = np.array(output[pos[0]][pos[1]][pos[2]][(j*5)+1:(j*5)+5], dtype=float)
+
+                    x_centre = (np.array(pos[1], dtype=float) + output_box[0])*32
+                    y_centre = (np.array(pos[2], dtype=float) + output_box[1])*32
+
+                    x_width, y_height = abs(W*output_box[2]), abs(H*output_box[3])
+
+                    x_min, y_min = int(x_centre - (x_width/2)), int(y_centre-(y_height/2))
+                    x_max, y_max = int(x_centre+(x_width/2)), int(y_centre + (y_height/2))
+
+                    if(x_min<0):x_min=0
+                    if(y_min<0):y_min=0
+                    if(x_max<0):x_max=0
+                    if(y_max<0):y_max=0
+
+                    final_boxes.append(
+                        [x_min, y_min, x_max, y_max, str(classes[np.argmax(selected_output[..., 10:], axis=-1)[i]])]
+                    )
+                    final_scores.append(selected_output[i][j*5])
+
+        final_boxes = np.array(final_boxes)
+
+        nms_output = nms(final_boxes, final_scores, 0.3)
+
+        print(nms_output)
+        for i in nms_output:
+            cv2.rectangle(
+                img,
+                (int(i[0]), int(i[1])),
+                (int(i[2]), int(i[3])), (255, 0, 0)
+            )
+
+            cv2.putText(
+                img,
+                i[-1],
+                (int(i[0]), int(i[1])+15),
+                cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), 1
+            )
+
+        output_pil_img = Image.fromarray(np.uint8(img)).convert('RGB')
+
+        return output_pil_img
+
+    except:
+        return input_image

model/yolo_efficient.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:94649900941ab5e8e91d9446ca9ee8d5f3c974f13b7357bd8ded5a297ac797b3
+size 132372993

requirements.txt

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+onnxruntime
+opencv-python-headless