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Added a way to crop image in standard size for all images in batch and final output images will be divisible by 8

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This commit is contained in:
Ram Deshmukh
2024-04-25 15:11:04 +05:30
parent 7595071a3c
commit 5fd4064b1e
9 changed files with 914 additions and 22 deletions
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__init__.py
+42 -22
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@@ -400,7 +400,7 @@ class TRI3DExtractPartsBatch:
# If no contours were found, just return None
if not contours:
return None
return None, None
# Combine all contours to find encompassing bounding box
all_points = np.concatenate(contours, axis=0)
@@ -416,9 +416,9 @@ class TRI3DExtractPartsBatch:
print(x, y, w, h, "x,y,w,h")
print(input_img.shape, "input_img.shape")
# Extract the region from the original image that contains both hands
hand_region = input_img[y:y + h, x:x + w]
return hand_region
# hand_region = input_img[y:y + h, x:x + w]
return input_img, [x,y,w,h]
def tensor_to_cv2_img(tensor, remove_alpha=False):
# This will give us (H, W, C)
@@ -436,6 +436,9 @@ class TRI3DExtractPartsBatch:
batch_results = []
images = []
secondaries = []
images_xywh = []
secondaries_xywh = []
# cv2_secondary = tensor_to_cv2_img(batch_secondaries)
for i in range(batch_images.shape[0]):
image = batch_images[i]
@@ -484,18 +487,20 @@ class TRI3DExtractPartsBatch:
if scarf:
color_code_list.append([128, 64, 0])
bimage = bounded_image(cv2_seg, color_code_list, cv2_image)
bsecondary = bounded_image(cv2_seg, color_code_list, cv2_secondary)
bimage, img_xywh = bounded_image(cv2_seg, color_code_list, cv2_image)
bsecondary, sec_xywh = bounded_image(cv2_seg, color_code_list, cv2_secondary)
# Handle case when bimage is None to avoid error during conversion to tensor
if bimage is not None:
images.append(bimage)
images_xywh.append(img_xywh)
else:
num_channels = cv2_image.shape[2] if len(
cv2_image.shape) > 2 else 1
black_img = np.zeros((10, 10, num_channels),
dtype=cv2_image.dtype)
images.append(black_img)
images_xywh.append([0, 0, 10, 10])
if bsecondary is not None:
secondaries.append(bsecondary)
@@ -507,28 +512,43 @@ class TRI3DExtractPartsBatch:
secondaries.append(black_img)
# Get max height and width
max_height = max(img.shape[0] for img in images)
max_width = max(img.shape[1] for img in images)
max_height = max([xywh[-1] for xywh in images_xywh])
max_width = max([xywh[-2] for xywh in images_xywh])
for i,img in enumerate(images): #this takes care of edge case where max crop height/width of batch
#exceeds image size of few images
x,y,_,_ = images_xywh[i]
h,w,_ = img.shape
if x+max_width > w:
max_width = w - x
if y+max_height > h:
max_height = h - y
max_height = max_height - max_height % 8 ##Making it divisible by 8
max_width = max_width - max_width % 8
batch_results = []
batch_secondaries = []
for img in images:
# Resize the image to max height and width
resized_img = cv2.resize(img, (max_width, max_height),
interpolation=cv2.INTER_CUBIC)
tensor_img = cv2_img_to_tensor(resized_img)
batch_results.append(tensor_img.squeeze(0))
for sec in secondaries:
# Resize the image to max height and width
resized_sec = cv2.resize(sec, (max_width, max_height),
interpolation=cv2.INTER_NEAREST)
tensor_sec = cv2_img_to_tensor(resized_sec)
batch_secondaries.append(tensor_sec.squeeze(0))
for i,img in enumerate(images):
x,y,w,h = images_xywh[i]
img = img[y:y+max_height, x:x+max_width]
img = cv2_img_to_tensor(img)
batch_results.append(img.squeeze(0))
for i,sec in enumerate(secondaries):
x,y,w,h = images_xywh[i]
sec = sec[y:y+max_height, x:x+max_width]
sec = cv2_img_to_tensor(sec)
batch_secondaries.append(sec.squeeze(0))
batch_results = torch.stack(batch_results)
batch_secondaries = torch.stack(batch_secondaries)
print(batch_results.shape, "batch_results.shape")
return (batch_results, batch_secondaries)
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samples/3.1_resize_extract_parts/test_workflow.json
+872
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@@ -0,0 +1,872 @@
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