change object_detector.py

object_detector.py

@@ -1,4 +1,4 @@
-from ultralytics import YOLO
+#from ultralytics import YOLO
 from flask import request, Flask, jsonify
 from waitress import serve
 from PIL import Image
@@ -44,14 +44,17 @@ def detect():
         :return: a JSON array of objects bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
     """
     buf = request.files["image_file"]
-    boxes = detect_objects_on_image(buf.stream)
-    print(boxes)
+    boxes, orientation = detect_objects_on_image(buf.stream)
+    #print(boxes)
+    #print(orientation)
     return jsonify(boxes)
 
 def detect_objects_on_image(buf):
     input, img_width, img_height = prepare_input(buf)
     output = run_model(input)
-    return process_output(output,img_width,img_height)
+    orientation = get_orientation(buf)
+    processed_output = process_output(output, img_width, img_height, orientation)
+    return processed_output, orientation
 
 def prepare_input(buf):
     img = Image.open(buf)
@@ -68,7 +71,7 @@ def run_model(input):
     outputs = model.run(["output0"], {"images":input})
     return outputs[0]
 
-def process_output(output, img_width, img_height):
+def process_output(output, img_width, img_height, orientation):
     output = output[0].astype(float)
     output = output.transpose()
 
@@ -77,6 +80,7 @@ def process_output(output, img_width, img_height):
         prob = row[4:].max()
         if prob < 0.5:
             continue
+
         class_id = row[4:].argmax()
         label = yolo_classes[class_id]
         xc, yc, w, h = row[:4]
@@ -85,20 +89,18 @@ def process_output(output, img_width, img_height):
         x2 = (xc + w/2) / 640 * img_width
         y2 = (yc + h/2) / 640 * img_height
 
-        rotated_x1 = img_height - y2
-        rotated_y1 = x1
-        rotated_x2 = img_height - y1
-        rotated_y2 = x2
+        boxes.append([x1, y1, x2, y2, label, prob])
 
-        boxes.append([rotated_x1, rotated_y1, rotated_x2, rotated_y2, label, prob])
+    # Adjust boxes based on orientation
+    adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
 
-        #boxes.append([x1, y1, x2, y2, label, prob])
-
-    boxes.sort(key=lambda x: x[5], reverse=True)
+    # Sort and apply non-max suppression as before
+    adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
     result = []
-    while len(boxes) > 0:
-        result.append(boxes[0])
-        boxes = [box for box in boxes if iou(box, boxes[0]) < 0.7]
+    while len(adjusted_boxes) > 0:
+        result.append(adjusted_boxes[0])
+        adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
+
     return result
 
 
@@ -121,6 +123,31 @@ def intersection(box1,box2):
     y2 = min(box1_y2,box2_y2)
     return (x2-x1)*(y2-y1)
 
+def get_orientation(image_path):
+    with Image.open(image_path) as img:
+        if hasattr(img, '_getexif'):
+            exif_data = img._getexif()
+            if exif_data is not None:
+                return exif_data.get(274, 1)  # Default to normal orientation
+    return 1  # Default orientation if no EXIF data
+
+def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
+    adjusted_boxes = []
+    for box in boxes:
+        x1, y1, x2, y2, label, prob = box
+
+        # Apply transformations based on orientation
+        if orientation == 3:  # 180 degrees
+            x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
+        elif orientation == 6:  # 270 degrees (or -90 degrees)
+            x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
+        elif orientation == 8:  # 90 degrees
+            x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
+
+        adjusted_boxes.append([x1, y1, x2, y2, label, prob])
+
+    return adjusted_boxes
+
 
 """ def detect_objects_on_image(buf):
     """