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Welspun-Classification
Commit a1998435 authored by dasharatha.vamshi's avatar dasharatha.vamshi
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parent cdb4a0a2
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Showing with 44 additions and 26 deletions
scripts/welspun_classifier.py
View file @ a1998435
...@@ -234,22 +234,28 @@ class Welspun_Classifier(ModelWrapper): ...@@ -234,22 +234,28 @@ class Welspun_Classifier(ModelWrapper):
fontScale=1, fontFace=cv2.LINE_AA) fontScale=1, fontFace=cv2.LINE_AA)
# pass # pass
else: else:
self.defect_type = 'Mix' # self.defect_type = 'Mix'
resized_frame = cv2.resize(frame, (64, 64)) resized_frame = cv2.resize(frame, (64, 64))
cv2.putText(frame, text="Mix Color Defect Detected", org=(50, 50), # cv2.putText(frame, text="Mix Color Defect Detected", org=(50, 50),
color=(0, 0, 255), # color=(0, 0, 255),
thickness=2, # thickness=2,
fontScale=1, fontFace=cv2.LINE_AA) # fontScale=1, fontFace=cv2.LINE_AA)
self.send_payload("Mix Color Detected", resized_frame, "Mix Color " + str(prob[0]), "#472020", # self.send_payload("Mix Color Detected", resized_frame, "Mix Color " + str(prob[0]), "#472020",
"#ed2020", # "#ed2020",
"sound_1") # "sound_1")
logger.info(f"Probability: {prob}") # logger.info(f"Probability: {prob}")
# if self.counter % 50 == 0: if self.counter % 25 == 0:
# self.send_payload("Mix Color Detected", resized_frame, "Mix Color " + str(prob[0]), "#472020", self.defect_type = 'Mix'
# "#ed2020", resized_frame = cv2.resize(frame, (64, 64))
# "sound_1") cv2.putText(frame, text="Mix Color Defect Detected", org=(50, 50),
# logger.info(f"Probability: {prob}") color=(0, 0, 255),
# self.counter = 0 thickness=2,
fontScale=1, fontFace=cv2.LINE_AA)
self.send_payload("Mix Color Detected", resized_frame, "Mix Color " + str(prob[0]), "#472020",
"#ed2020",
"sound_1")
logger.info(f"Probability: {prob}")
self.counter = 0
elif a == 2 and x[2] > 0.95: elif a == 2 and x[2] > 0.95:
if self.defect_type == 'Short': if self.defect_type == 'Short':
cv2.putText(frame, text="Short Defect Detected", org=(50, 50), cv2.putText(frame, text="Short Defect Detected", org=(50, 50),
...@@ -258,6 +264,17 @@ class Welspun_Classifier(ModelWrapper): ...@@ -258,6 +264,17 @@ class Welspun_Classifier(ModelWrapper):
fontScale=1, fontFace=cv2.LINE_AA) fontScale=1, fontFace=cv2.LINE_AA)
# pass # pass
else: else:
# self.defect_type = 'Short'
resized_frame = cv2.resize(frame, (64, 64))
# cv2.putText(frame, text="Short Defect Detected", org=(50, 50),
# color=(0, 0, 255),
# thickness=2,
# fontScale=1, fontFace=cv2.LINE_AA)
# self.send_payload("Short Tile Detected", resized_frame, "Short Tile " + str(prob[2]), "#472020",
# "#ed2020",
# "sound_1")
# logger.info(f"Probability: {prob}")
if self.counter % 25 == 0:
self.defect_type = 'Short' self.defect_type = 'Short'
resized_frame = cv2.resize(frame, (64, 64)) resized_frame = cv2.resize(frame, (64, 64))
cv2.putText(frame, text="Short Defect Detected", org=(50, 50), cv2.putText(frame, text="Short Defect Detected", org=(50, 50),
...@@ -268,12 +285,7 @@ class Welspun_Classifier(ModelWrapper): ...@@ -268,12 +285,7 @@ class Welspun_Classifier(ModelWrapper):
"#ed2020", "#ed2020",
"sound_1") "sound_1")
logger.info(f"Probability: {prob}") logger.info(f"Probability: {prob}")
# if self.counter % 50 == 0: self.counter = 0
# self.send_payload("Short Tile Detected", resized_frame, "Short Tile " + str(prob[2]), "#472020",
# "#ed2020",
# "sound_1")
# logger.info(f"Probability: {prob}")
# self.counter = 0
elif a == 3 and x[3] > 0.95: elif a == 3 and x[3] > 0.95:
if self.defect_type == 'Split': if self.defect_type == 'Split':
cv2.putText(frame, text="Split Defect Detected", org=(50, 50), cv2.putText(frame, text="Split Defect Detected", org=(50, 50),
...@@ -282,6 +294,16 @@ class Welspun_Classifier(ModelWrapper): ...@@ -282,6 +294,16 @@ class Welspun_Classifier(ModelWrapper):
fontScale=1, fontFace=cv2.LINE_AA) fontScale=1, fontFace=cv2.LINE_AA)
# pass # pass
else: else:
# self.defect_type = 'Split'
resized_frame = cv2.resize(frame, (64, 64))
# cv2.putText(frame, text="Split Defect Detected", org=(50, 50),
# color=(0, 0, 255),
# thickness=2,
# fontScale=1, fontFace=cv2.LINE_AA)
# self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020",
# "sound_1")
# logger.info(f"Probability: {prob}")
if self.counter % 25 == 0:
self.defect_type = 'Split' self.defect_type = 'Split'
resized_frame = cv2.resize(frame, (64, 64)) resized_frame = cv2.resize(frame, (64, 64))
cv2.putText(frame, text="Split Defect Detected", org=(50, 50), cv2.putText(frame, text="Split Defect Detected", org=(50, 50),
...@@ -291,11 +313,7 @@ class Welspun_Classifier(ModelWrapper): ...@@ -291,11 +313,7 @@ class Welspun_Classifier(ModelWrapper):
self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020", self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020",
"sound_1") "sound_1")
logger.info(f"Probability: {prob}") logger.info(f"Probability: {prob}")
# if self.counter % 50 == 0: self.counter = 0
# self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020",
# "sound_1")
# logger.info(f"Probability: {prob}")
# self.counter = 0
elif a == 2: elif a == 2:
pass pass
......
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