assert img.shape[2] == 3
时间: 2024-06-06 19:07:45 浏览: 21
This line of code is checking if the third dimension of the image array is equal to 3. This is because a standard RGB image has three color channels: red, green, and blue. If the third dimension is not equal to 3, it means that the image is not a standard RGB image and may have a different number of color channels or be in a different color space.
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for filename in calib_files: img = self.imread(filename) if img is None: raise FileNotFoundError(filename, "没有发现!") if len(img.shape) == 2: gray = img else: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if self.img_size is None: self.img_size = gray.shape[::-1] else: assert gray.shape[::-1] == self.img_size
这段代码使用了一个for循环遍历calib_files中的每个文件名,并进行以下操作:
1. 调用self.imread函数读取文件,并将结果赋值给img变量。
2. 如果img为空,即读取文件失败,则抛出FileNotFoundError异常。
3. 如果img是二维的(即灰度图像),则将其赋值给gray变量。
4. 如果img是三维的(即彩色图像),则将其转换为灰度图像,并将结果赋值给gray变量。
5. 如果self.img_size为None,则将gray的shape取反(即高度和宽度交换)作为img_size。
6. 如果self.img_size不为None,则使用assert语句检查gray的shape取反是否等于img_size。如果不等于,则抛出AssertionError异常。
这段代码的作用是读取一组图像文件,并将它们转换为相同大小的灰度图像。如果读取文件失败或者图像大小不一致,则会抛出异常。
上述问题,在文件里函数如下,请指出在哪里修改路径load_image(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img = self.imgs[index] if img is None: # not cached path = self.img_files[index] img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h0, w0 = img.shape[:2] # orig hw r = self.img_size / max(h0, w0) # resize image to img_size if r != 1: # always resize down, only resize up if training with augmentation interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized else: return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
在上述代码中,你可以在 `load_image()` 函数中修改图像路径。具体来说,你需要修改 `path = self.img_files[index]` 这一行的代码。将其中的 `self.img_files[index]` 更改为数据集中实际的图像路径。
请确保修改后的路径与数据集中实际的图像路径一致,以确保正确加载数据集中的图像文件。
以下是修改后的代码示例:
```python
# loads 1 image from dataset, returns img, original hw, resized hw
img = self.imgs[index]
if img is None: # not cached
path = "数据集中的实际图像路径" # 修改为数据集中实际的图像路径
img = cv2.imread(path) # BGR
assert img is not None, 'Image Not Found ' + path
h0, w0 = img.shape[:2] # orig hw
r = self.img_size / max(h0, w0) # resize image to img_size
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized
else:
return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
```
请将 "数据集中的实际图像路径" 替换为数据集中对应图像的路径。保存修改后的文件,并重新运行 train.py 文件。