PyTorch Hub cv2 .save() .show() bug fix

hubconf.py

@@ -124,13 +124,15 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True):
     # model = custom(path_or_model='path/to/model.pt')  # custom example
 
     # Verify inference
+    import cv2
     import numpy as np
     from PIL import Image
 
-    imgs = [Image.open('data/images/bus.jpg'),  # PIL
-            'data/images/zidane.jpg',  # filename
-            'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg',  # URI
-            np.zeros((640, 480, 3))]  # numpy
+    imgs = ['data/images/zidane.jpg',  # filename
+            'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg',  # URI
+            cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
+            Image.open('data/images/bus.jpg'),  # PIL
+            np.zeros((320, 640, 3))]  # numpy
 
     results = model(imgs)  # batched inference
     results.print()

models/common.py

@@ -240,7 +240,7 @@ def autoshape(self):
     @torch.no_grad()
     def forward(self, imgs, size=640, augment=False, profile=False):
         # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
-        #   filename:   imgs = 'data/samples/zidane.jpg'
+        #   filename:   imgs = 'data/images/zidane.jpg'
         #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
         #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
         #   PIL:             = Image.open('image.jpg')  # HWC x(640,1280,3)
@@ -271,7 +271,7 @@ def forward(self, imgs, size=640, augment=False, profile=False):
             shape0.append(s)  # image shape
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
-            imgs[i] = im  # update
+            imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
         shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
         x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack

utils/plots.py

@@ -54,32 +54,34 @@ def butter_lowpass(cutoff, fs, order):
     return filtfilt(b, a, data)  # forward-backward filter
 
 
-def plot_one_box(x, img, color=None, label=None, line_thickness=3):
-    # Plots one bounding box on image img
-    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+def plot_one_box(x, im, color=None, label=None, line_thickness=3):
+    # Plots one bounding box on image 'im' using OpenCV
+    assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
+    tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1  # line/font thickness
     color = color or [random.randint(0, 255) for _ in range(3)]
     c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
-    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+    cv2.rectangle(im, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
     if label:
         tf = max(tl - 1, 1)  # font thickness
         t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
         c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+        cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
 
 
-def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None):
-    img = Image.fromarray(img)
-    draw = ImageDraw.Draw(img)
-    line_thickness = line_thickness or max(int(min(img.size) / 200), 2)
+def plot_one_box_PIL(box, im, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image 'im' using PIL
+    im = Image.fromarray(im)
+    draw = ImageDraw.Draw(im)
+    line_thickness = line_thickness or max(int(min(im.size) / 200), 2)
     draw.rectangle(box, width=line_thickness, outline=tuple(color))  # plot
     if label:
-        fontsize = max(round(max(img.size) / 40), 12)
+        fontsize = max(round(max(im.size) / 40), 12)
         font = ImageFont.truetype("Arial.ttf", fontsize)
         txt_width, txt_height = font.getsize(label)
         draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color))
         draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font)
-    return np.asarray(img)
+    return np.asarray(im)
 
 
 def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()