Update detection_utils.py

detection_utils.py

@@ -1,69 +1,66 @@
-import io
-import matplotlib.pyplot as plt
-import requests
-import inflect
-from PIL import Image
-
-def load_image_from_url(url):
-    return Image.open(requests.get(url, stream = True).raw)
-
-def render_results_in_image(in_pil_img, in_results):
-    plt.figure(figsize=(16,10))
-    plt.imshow(in_pil_img)
-
-    ax = plt.gca()
-
-    for prediction in in_results:
-        x, y = prediction['box']['xmin'], prediction['box']['ymin']
-        w = prediction['box']['xmax'] - prediction['box']['xmin']
-        h = prediction['box']['ymax'] - prediction['box']['ymin']
-
-        ax.add_patch(plt.Rectangle(
-            (x,y),
-            w,
-            h,
-            fill = False,
-            color = 'green',
-            linewidth = 2
-        ))
-
-        ax.text(
-            x,
-            y,
-            f"{prediction['label']}: {round(prediction['score']*100,1)}%",
-            color = 'red'
-        )
-    plt.axis('off')
-
-    img_buf = io.BytesIO()
-    plt.savefig(img_buf, format = 'png', bbox_inches = 'tight', pad_inches = 0)
-    img_buf.seek(0)
-    modified_image = Image.open(img_buf)
-    plt.close()
-    return modified_image
-
-
-def summarize_predictions_natural_language(predictions):
-    summary = {}
-    p = inflect.engine()
-
-    for prediction in predictions:
-        label = prediction['label']
-        if label in summary:
-            summary[label] += 1
-        else:
-            summary[label] = 1
-
-    result_string = "In this image, there are "
-    for i, (label, count) in enumerate(summary.items()):
-        count_string = p.number_to_words(count)
-        result_string += f"{count_string} {label}"
-
-        if count > 1:
-            result_string += 's'
-        result_string += " "
-
-        if i == len(summary) - 2:
-            result_string += "and "
-    result_string = result_string.rstrip(', ') + "."
+import io
+import matplotlib.pyplot as plt
+import inflect
+from PIL import Image
+
+
+def render_results_in_image(in_pil_img, in_results):
+    plt.figure(figsize=(16,10))
+    plt.imshow(in_pil_img)
+
+    ax = plt.gca()
+
+    for prediction in in_results:
+        x, y = prediction['box']['xmin'], prediction['box']['ymin']
+        w = prediction['box']['xmax'] - prediction['box']['xmin']
+        h = prediction['box']['ymax'] - prediction['box']['ymin']
+
+        ax.add_patch(plt.Rectangle(
+            (x,y),
+            w,
+            h,
+            fill = False,
+            color = 'green',
+            linewidth = 2
+        ))
+
+        ax.text(
+            x,
+            y,
+            f"{prediction['label']}: {round(prediction['score']*100,1)}%",
+            color = 'red'
+        )
+    plt.axis('off')
+
+    img_buf = io.BytesIO()
+    plt.savefig(img_buf, format = 'png', bbox_inches = 'tight', pad_inches = 0)
+    img_buf.seek(0)
+    modified_image = Image.open(img_buf)
+    plt.close()
+    return modified_image
+
+
+def summarize_predictions_natural_language(predictions):
+    summary = {}
+    p = inflect.engine()
+
+    for prediction in predictions:
+        label = prediction['label']
+        if label in summary:
+            summary[label] += 1
+        else:
+            summary[label] = 1
+
+    result_string = "In this image, there are "
+    for i, (label, count) in enumerate(summary.items()):
+        count_string = p.number_to_words(count)
+        result_string += f"{count_string} {label}"
+
+        if count > 1:
+            result_string += 's'
+        result_string += " "
+
+        if i == len(summary) - 2:
+            result_string += "and "
+    result_string = result_string.rstrip(', ') + "."
     return result_string