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Welspun-Classification
Commit dc572c36 authored by dasharatha.vamshi's avatar dasharatha.vamshi
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good sample to no defect

parent 90517f90
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scripts/model/resnet34-wel4.mapping 0 → 100644
View file @ dc572c36
<?xml version="1.0" ?>
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<IR id="96" name="Conv_41" out_port_id="2"/>
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scripts/model/resnet34-wel4.xml 0 → 100644
View file @ dc572c36
This source diff could not be displayed because it is too large. You can view the blob instead.
scripts/welspun_classifier.py
View file @ dc572c36
...@@ -99,7 +99,7 @@ class Welspun_Classifier(ModelWrapper): ...@@ -99,7 +99,7 @@ class Welspun_Classifier(ModelWrapper):
try: try:
frame = obj['frame'] frame = obj['frame']
orig_image = cv2.resize(frame, (3840, 2160)) orig_image = cv2.resize(frame, (800, 600))
frame = self.process_frame(orig_image) frame = self.process_frame(orig_image)
obj['frame'] = cv2.resize(frame, (self.config.get('FRAME_WIDTH'), self.config.get('FRAME_HEIGHT'))) obj['frame'] = cv2.resize(frame, (self.config.get('FRAME_WIDTH'), self.config.get('FRAME_HEIGHT')))
...@@ -190,7 +190,7 @@ class Welspun_Classifier(ModelWrapper): ...@@ -190,7 +190,7 @@ class Welspun_Classifier(ModelWrapper):
def process_frame(self, frame): def process_frame(self, frame):
starttime = time.time() starttime = time.time()
vino_frame = frame.copy() vino_frame = frame.copy()
vino_frame = vino_frame[20:600,150:650]
images = np.ndarray(shape=(self.n, self.c, self.h, self.w)) images = np.ndarray(shape=(self.n, self.c, self.h, self.w))
images_hw = [] images_hw = []
for i in range(self.n): for i in range(self.n):
...@@ -232,7 +232,6 @@ class Welspun_Classifier(ModelWrapper): ...@@ -232,7 +232,6 @@ class Welspun_Classifier(ModelWrapper):
thickness=2, thickness=2,
fontScale=1, fontFace=cv2.LINE_AA) fontScale=1, fontFace=cv2.LINE_AA)
if self.counter % 25 == 0: if self.counter % 25 == 0:
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")
...@@ -245,23 +244,25 @@ class Welspun_Classifier(ModelWrapper): ...@@ -245,23 +244,25 @@ class Welspun_Classifier(ModelWrapper):
thickness=2, thickness=2,
fontScale=1, fontFace=cv2.LINE_AA) fontScale=1, fontFace=cv2.LINE_AA)
if self.counter % 25 == 0: if self.counter % 25 == 0:
self.send_payload("Short Tile Detected", resized_frame, "Short Tile " + str(prob[2]), "#472020",
self.send_payload("Split Detected", resized_frame, "Split " + str(prob[1]), "#472020", "#ed2020", "#ed2020",
"sound_1") "sound_1")
logger.info(f"Probability: {prob}") logger.info(f"Probability: {prob}")
self.counter = 0 self.counter = 0
elif a == 1 and x[1] > 0.95: elif a == 3 and x[3] > 0.95:
resized_frame = cv2.resize(frame, (64, 64)) resized_frame = cv2.resize(frame, (64, 64))
cv2.putText(frame, text="Short Tile Defect Detected", org=(50, 50), cv2.putText(frame, text="Split 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)
if self.counter % 25 == 0: if self.counter % 25 == 0:
self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020",
self.send_payload("Short Tile Detected", resized_frame, "Short Tile " + str(prob[1]), "#472020", "#ed2020",
"sound_1") "sound_1")
logger.info(f"Probability: {prob}") logger.info(f"Probability: {prob}")
self.counter = 0 self.counter = 0
elif a == 2:
pass
# else: # else:
# logger.info("enter else loop") # logger.info("enter else loop")
# if sum(sum(sum(frame))) == 0: # if sum(sum(sum(frame))) == 0:
......
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